http://2012.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=50&target=Abush842012.igem.org - User contributions [en]2020-04-07T04:44:26ZFrom 2012.igem.orgMediaWiki 1.16.0http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T04:04:47Z<p>Abush84: /* At different initial OD and proportions */</p>
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<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|-<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|-<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase. Therefore we decided to use epifluorescence microscopy (see below).<br />
<br />
== Strains proportion measurement ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains TCY-3281 (that expresses YFP) and TCY-3265 (that expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in the figure and table there is a basal growth that does not depend on the initial OD or strain proportion. This residual growth produces a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. This suggests that at these proportions there is a natural cooperation between the strains. The objective of the project is to build upon this natural cooperation and to allow for tunable proportions.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T03:59:21Z<p>Abush84: /* Strain proportion measurement */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|-<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|-<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase. Therefore we decided to use epifluorescence microscopy (see below).<br />
<br />
== Strains proportion measurement ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains TCY-3281 (that expresses YFP) and TCY-3265 (that expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in graph and table there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T03:58:42Z<p>Abush84: /* Screening of strain proportion */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|-<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|-<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase. Therefore we decided to use epifluorescence microscopy (see below).<br />
<br />
== Strain proportion measurement ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains TCY-3281 (that expresses YFP) and TCY-3265 (that expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in graph and table there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T03:57:00Z<p>Abush84: /* Measurement of strains fluorescence */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|-<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|-<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase. Therefore we decided to use epifluorescence microscopy (see below).<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in graph and table there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T03:54:53Z<p>Abush84: /* Description of strains */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|-<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|-<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in graph and table there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-10-27T03:54:25Z<p>Abush84: /* Description of strains */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|TCY 3128<br />
| style="text-align: center;" |(-H-T)<br />
|CFP <br />
|His device testing<br />
|TCY 3081<br />
| style="text-align: center;" |(-H-T)<br />
|YFP <br />
|Trp device testing<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurement of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Fluoro- igembsas2012. strains.png|650px]]<br />
|-<br />
|'''CFP Fluorescence Screening and YFP Fluorescence Screening'''<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control Mix 2: 80% CFP; 20%YFP Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP Mix 5: 40% CFP; 60%YFP Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|100px]]<br />
|[[File:Bsas2012-strains-figura2.png|100px]]<br />
|[[File:Bsas2012-strains-figura4.png|100px]]<br />
|[[File:Bsas2012-strains-figura5.png|100px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:HIS-BSAS2012.png|400px]]<br />
|}<br />
<br />
<br />
As shown in graph and table there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 20μg/ml, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting and diffusing their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Curva.png | 250px]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.01μg/ml, and as high as 20μg/ml, maybe more. Since our medium is 20μg/ml, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|250px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|250px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
<br />
== Experimental determination of strains death rate==<br />
<br />
We set out to determine how long can auxotroph cells[link] survive in media that lacks both Trytophan and Histidine. These values are the '''death''' parameters for CFP and YFP strains used in our model[link]. These were taken as equal in the mathematical analysis for simplicity but now we would like to test whether this approximation is accurate.<br />
<br />
Given that our system most likely will present a lag phase until a certain amount of both AmioAcids is accumulated in the media, will the cells be viable until this occurs? This is a neccesary check of our '' system's feasability''.<br />
<br />
===== Protocol =====<br />
<br />
For this experiment we used<br />
{|<br />
|-<br />
|[[File:BsAs2012-icono-YFP.jpg|200px]]<br />
|[[File:BsAs2012-icono-CFP.jpg|200px]]<br />
|- align="center"<br />
|YFP Strain<br />
|CFP Strain<br />
|}<br />
<br />
*We set cultures of the two auxotroph strains without being transformed (YFP and CFP) in medium –HT at an initial OD of 0.01. <br />
*Each day we plated the same amount of µl of the culture and counted the number of colonies obtain in each plate. We set 3 replica of each strain.<br />
<br />
===== Result =====<br />
<br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" |Strain<br />
! scope="row" style="background: #7ac5e8" |Replica<br />
! scope="row" style="background: #7ac5e8" |Monday<br />
! scope="row" style="background: #7ac5e8" |Tuesday<br />
! scope="row" style="background: #7ac5e8" |Wednesday<br />
|-<br />
|CFP<br />
|1<br />
|260<br />
|320<br />
|285<br />
|-<br />
|CFP<br />
|2<br />
|267<br />
|314<br />
|76<br />
|-<br />
|CFP<br />
|3<br />
|413<br />
|362<br />
|278<br />
|-<br />
|YFP<br />
|1<br />
|230<br />
|316<br />
|688<br />
|-<br />
|YFP<br />
|2<br />
|291<br />
|194<br />
|524<br />
|-<br />
|YFP<br />
|3<br />
|449<br />
|344<br />
|725<br />
|}<br />
<br />
'''Table:''' Number of colonies counted per plate.<br />
<br />
We expected to see a decrease in the number of colonies because of cell death. We found that this was not the case in the experiment's time lapse. However we observed that the size of the colonies was smaller everyday as can be seen in the following pictures.<br />
<br />
[[File:Bsas2012kdeathcells.png| 500px]]<br />
<br />
<br />
We can infer from this data that though they have not died, they may have enter into a persistant state. In this way cells can survive for a period of time in media defficient in amino acid (at least, during the time course of our experiment), but grow slower. Probably this would require more time than 3 days to observe significative cell dying.<br />
<br />
These results are consistent with the chosen parameters. Moreover, the slower the death rate the bigger the area in the Parameter Space where regulation is feasable.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/AttributionsTeam:Buenos Aires/Attributions2012-10-27T03:47:31Z<p>Abush84: /* Attributions */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
= Attributions =<br />
<br />
* Peptides of payload devices ''PolyHb'' and ''PolyWb'' were designed by team instructors, although students got their dna sequences by retro translation, and added the parts to the registry<br />
<br />
* DNA was synthesized by IDT.<br />
<br />
* All yeast strains we worked with, and some reactants and equipment, were kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab.<br />
<br />
* '''Everything''' not mentioned explicitly before was done by student members of the team (with the advisors supervision)</div>Abush84http://2012.igem.org/File:Crossfeeding-v03-small.pngFile:Crossfeeding-v03-small.png2012-10-27T03:38:36Z<p>Abush84: </p>
<hr />
<div></div>Abush84http://2012.igem.org/Team:Buenos_AiresTeam:Buenos Aires2012-09-27T03:57:12Z<p>Abush84: </p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Welcome to Buenos Aires 2012 iGEM Wiki! =<br />
<br />
{|<br />
|<br />
'''What are you looking for?'''<br />
<br />
* Check the [[Team:Buenos_Aires/Safety|safety questions]] and [[ Team:Buenos_Aires/Attributions | attributions]].<br />
<br />
* Meet [[Team:Buenos_Aires/Team/Members | the team]]. You can also read a little about [[Team:Buenos_Aires/Team/BsAs | where we come from]].<br />
<br />
* Learn about [[Team:Buenos_Aires/Project | our project]] (and don't forget to check [[Team:Buenos_Aires/Project/Schemes | all the schemes]] we thought to solve the problem).<br />
<br />
* Don't miss our yeast [[Team:Buenos_Aires/Results/Strains| strains characterization]], and the [[Team:Buenos_Aires/Results/Bb1 |main biobricks and devices ]] we designed and added to the registry. You can also find information about our ''planB'' [[Team:Buenos_Aires/Results/Bb2 | backup biobrick]] ''(aka. thank-you-customs biobrick)'' .<br />
<br />
* Take a look at our [[Team:Buenos_Aires/Project/Model | mathematical model]] of a synthetic ecology. And if you dare, take a look at the [[Team:Buenos_Aires/Project/ModelAdvance | advanced model]].<br />
<br />
* What we did outside the lab ''(aka. human practices)'' to [[Team:Buenos_Aires/HP/GarageLab | teach what synBio is about]], [[Team:Buenos_Aires/HP/GarageLab | start solving local problems ]] and [[ Team:Buenos_Aires/HP/EMBO | seed SynBio in Latin America]].<br />
<br />
* Watch an [http://youtu.be/bkczB60RziU online presentation] of our team and our project.<br />
<br />
* If you understand spanish, you can also check our [http://www.youtube.com/watch?v=oEMXc6cmmgo presentation video].<br />
<br />
* Some [http://blogs.scientificamerican.com/lab-rat/2012/09/09/igem-buenos-aires-synthetic-bacterial-communities/ impact in the online media ] (Scientific American blog)<br />
<br />
|<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-The Team.png|400px]]<br />
|}<br />
<br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_AiresTeam:Buenos Aires2012-09-27T03:56:25Z<p>Abush84: </p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Welcome to Buenos Aires 2012 iGEM Wiki! =<br />
<br />
{|<br />
|<br />
'''What are you looking for?'''<br />
<br />
* Check the [[Team:Buenos_Aires/Safety|safety questions]] and [[ Team:Buenos_Aires/Attributions | attributions]].<br />
<br />
* Meet [[Team:Buenos_Aires/Team/Members | the team]]. You can also read a little about [[Team:Buenos_Aires/Team/BsAs | where we come from]].<br />
<br />
* Learn about [[Team:Buenos_Aires/Project | our project]] (and don't forget to check [[Team:Buenos_Aires/Project/Schemes | all the schemes]] we thought to solve the problem).<br />
<br />
* Don't miss our yeast [[Team:Buenos_Aires/Results/Strains| strains characterization]], and the [[Team:Buenos_Aires/Results/Bb1 |main biobricks and devices ]] we designed and added to the registry. You can also find information about our ''planB'' [[Team:Buenos_Aires/Results/Bb2 | backup biobrick]] ''(aka. thank-you-customs biobrick)'' .<br />
<br />
* Take a look at our [[Team:Buenos_Aires/Project/Model | mathematical model]] of a synthetic ecology. And if you dare, take a look at the [[Team:Buenos_Aires/Project/ModelAdvance | advanced model]].<br />
<br />
* What we did outside the lab ''(aka. human practices)'' to [[Team:Buenos_Aires/HP/GarageLab | teach what synBio is about]], [[Team:Buenos_Aires/HP/GarageLab | start solving local problems ]] and [[ Team:Buenos_Aires/HP/EMBO | seed SynBio in Latin America]].<br />
<br />
* Watch an [http://youtu.be/bkczB60RziU online presentation] of our team and our project.<br />
<br />
* If you understand spanish, you can also check our [http://www.youtube.com/watch?v=oEMXc6cmmgo presentation video].<br />
<br />
* Some [http://blogs.scientificamerican.com/lab-rat/2012/09/09/igem-buenos-aires-synthetic-bacterial-communities/ impact in the online media ] (ScientificAmerican)<br />
<br />
|<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-The Team.png|400px]]<br />
|}<br />
<br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Team/MembersTeam:Buenos Aires/Team/Members2012-09-27T03:53:44Z<p>Abush84: /* Advisors */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
{|<br />
| [[File:Bsas2012-banderaArgentina.png|100px|]]<br />
| align = "center" | '''We are the first Argentinian team to participate in iGEM competition, so everything is new for us! Here we are, ready to work hard on our project!'''. '''Meet us, and also check [[Team:Buenos_Aires/Team/BsAs | where we come from]] '''<br />
| [[File:Bsas2012-banderaArgentina.png|100px|]]<br />
<br />
|}<br />
<br />
== Students ==<br />
<br />
{| style="width:90%"<br />
|-<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Basas2012-Vero.jpg]]<br />
| rowspan="2" style="width: 3%; background: #009ee1;" |<br />
| style="width: 75%;" | '''Veronica Parasco - Physics Student'''<br />
|- valign="top"<br />
|I guess I like to study too much, or the stress and making summaries. I'm about to finish my Licentiate in Physics, when I started to think about what was next, I realized that several of the decision I made took me closer to other career fields. So here I am, learning biology and opening new doors.<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-mario.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #009ee1;" |<br />
| style="width: 75%;" | '''Mario J. Rugiero - Chemistry and Computer Sciece Student'''<br />
|- valign="top"<br />
|Initially, the iGEM grabbed my attention through its idea of free thought and colaboration, which I consider should be general rule if we want society to progress. Even though my career choice seems remotely related to synthetic biology, curiosity moves me to learn a little bit about everything, which is never a bad idea.<br />
I'd like to see that this initiative will bring together all the synthetic biology investigation groups in Argentina because I see in this discipline an opportunity to develop and resolve many local, and very important, problems.<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-ale.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #009ee1;" |<br />
| style="width: 75%;" | '''María Alejandra Parreño - Biology Student'''<br />
|- valign="top"<br />
|Within the vast field of Biology, i like ALL study subjects, but my specialty at the moment Biological Ecology of Populations (previously known as Population Genetics). Since 2009, I work in conserving the genetic variability of fruit flies which are a plague in Latin America, and I also do so with other insects with economical importance.<br />
I have another two very strong interests: on one hand, the spread of science, and on the other, the sustainable development of societies and handling of natural and biological resources. So I try to match my academic ocupations with relevant participations in congress and with activities in these two areas. I love competition, innovation, and challenges, which is what attracted me to participate in iGEM and test my abilities. I see an unexplored synthetic biology field in Argentina, with a great potential to solve scientific and social problems, using wits and creativity as the main tools. As a first iGEM group, we want to impulse these ideas into reality and start off with the right foot!<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-lucho.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #009ee1;" |<br />
| style="width: 75%;" | '''Luciano G. Morosi- Biology Student'''<br />
|- valign="top"<br />
| I am currently going through my last year, and i've been an intern in an investigation laboratory. From a very young age, I was passionate and interested in natural sciences, and I decided to study biology because you must have knowledge in all sciences to be able to understand it. In this way, my interest in synthetic biology comes from the fact that it is multidisciplinary, and I'm immensely attracted to the idea of being able to create biological systems – or based in biological parts- that are innovative, which carry out specifically designed actions, using and creating standarized and combined parts. I'm not only attracted to science, but also literature, theater, music and sports: for a very long time I participated in gymnastics and swimming, and I still do the latter. I love to write, I feel comfortable writing social, political and cultural papers, and also stories.<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-manugimenez.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #009ee1;" |<br />
| style="width: 75%;" | '''Manuel Giménez - Computer Science Student'''<br />
|- valign="top"<br />
| I'm writing my licentiate thesis on building a tool to automatically reason about regulations. Yes yes, nothing relates to biology. Weird, right? Nevertheless, I've always had an interest for this discipline, mainly molecular biology. When I found out about the existance of iGEM – a couple of months ago- I said ''we have to assemble a team in Argentina''. Coincidence or not, a week later I got an email inviting me to be a part of the first Argentinian iGEM team, and now I find myself taking my first steps in synthetic biology as a member of iGEM BsAs. I'm mainly interested in the engineering vision that synthetic biology has, and I believe from the computer science standpoint, I have several ideas I can bring to this new subject. I love scientific dissemination and teaching; I consider myself a straightforward communicator, and my natural way of working is in groups. I'm part of a political movements in my university, and I try to make my passing through this world the most transforming and engaging possible.<br />
|}<br />
<br />
== Advisors ==<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-alan.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #004a99;" |<br />
| style="width: 75%;" | '''Alan Bush - M.Sc. in Biology'''<br />
|- valign="top"<br />
|I'm M. Sc. in Biology and I'm currently doing my Ph. D. in Biology. My studies are in the field of "systems biology", an area which attempts to give a more quantitative and integrative approach to molecular biology, through the use of mathematic modeling tools. It is related to synthetic biology since it uses the same kind of tools and model organisms. However, the focus is radically different; while systems biology aims to understand how the cells work, the main objective of synthetic biology is to design and produce "biological devices" with a given behavior. My main motivation for participating as advisor for UBA's iGEM team is precisely this approach switch. I'm fascinated with the idea of using our knowledge to develop useful devices which can help to solve concrete problems.<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-german.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #004a99;" |<br />
| style="width: 75%;" | '''German Sabio - M.Sc. in Biology'''<br />
|- valign="top"<br />
|I'm an extremely curious person, and I've always had a passion for all kind of "bugs" (a highly academic and very complex concept, which includes everything from cell and virus to mammal and aliens) and how "life" works. A M. Sc. in Biology didn't gave me the answer just yet, but kept my curiosity appeased for some years and gave me a big ammount of tools to keep asking new questions.<br />
Currently, I work on a branch of biology dedicated to living being's development: how, from a single cell, or from a group of similar cells, a differentiated organism gets developed. My Ph. D. discipline is systems biology, which basically studies several biology areas looking for math patterns and predicting (or, actually, modelling) different living systems. Synthetic biology would be the other face of the same coin: while systems biology tries to find and define mechanisms in nature to understand how they work, synthetic biology tries to reproduce or generate new systems with a predetermined function.<br />
I'm very interested in this chance to take part on an iGEM team, not just for the ammount of tools it represents, but because it's a good and interesting experience to start a group and discuss and work together.<br />
|}<br />
<br />
== Instructors==<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-nacho.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #0B2161;" |<br />
| style="width: 75%;" | '''Ignacio E. Sánchez - M.Sc. in Chemistry and Ph.D. in Biophysics'''<br />
|- valign="top"<br />
|I'm a spanish bioinformatics specialist, currently living in Argentina. Before, I was an in training biophysicist on northern Europe, and before, a chemist, fascinated by biological molecules. Day by day, I study oncogenic virus in the Department of Biological Chemistry's Protein's Physiology Laboratory.<br />
I think synthetic biology is an excellent chance for developing countries to acquire new capabilities in the subject of biotechnology. Because of that reason, I joined Dr. Nadra in 2011 to promote the formation of synthetic biologists in Argentina. By now, the experience is being highly fun and rewarding.<br />
|}<br />
<br />
<br />
{| style="width:90%"<br />
|- valign="top"<br />
| rowspan="2" style="width: 20%; text-align: center;" | [[Image:Bsas2012-alenadra.jpg|160px]]<br />
| rowspan="2" style="width: 3%; background: #0B2161;" |<br />
| style="width: 75%;" | '''Alejandro D. Nadra - M.Sc. in Biology and Ph.D. in Chemistry'''<br />
|- valign="top"<br />
|I'm M. Sc. in Biology (2001) and Ph. D. in Chemistry (2005). My subjects of interest are protein's structure/function/folding and their interaction with nucleic acids. Also hemoproteins, evolution and synthetic biology, and others. I did a post-doc with the molecular modelling group in the FCEyN and another one in the systems biology program of the Genomic Regulation Center from Barcelona.<br />
I started to work as teacher in the FCEyN in 2000 and I'm currently Assistant Professor in the Department of Biological Chemistry and researcher at CONICET. I'm also a member of the Structural Biochemistry Laboratory, Department of Biological Chemistry.<br />
Given the lack of Synthetic Biology in Argentina, we are promoting the area with Ignacio Sánchez since 2011. For this effort, we organized the first post-graduate course in the subject in 2011, and conducted a course with featured international referents in April 2012. I'm convinced that from my place and given my formation, I can boost Synthetic Biology and begin formation of pure strain Synthetic Biologists. I believe the iGEM competitions are an excellente tool for the education and motivation of students. I'm convinced that from Argentina we can contribute to the subject and be on pair with teams from everywhere in the world. Even though we count on less resources and funds, this lack is compensated with a huge motivation and creativity.<br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:45:31Z<p>Abush84: /* Results */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|300px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
|}<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Parameter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:44:13Z<p>Abush84: /* Results */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Paramter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:41:58Z<p>Abush84: /* Growth dependence on the Trp and His concentrations */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Project/Model#Paramter_selection|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC).<br />
<br />
HLU and TLU are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:40:08Z<p>Abush84: /* Growth dependence on the Trp and His concentrations */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC (no cells)<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
| -T<br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
| Trp/2<br />
|2.56 <br />
|2.17<br />
|-<br />
| Trp/4<br />
|3.01 <br />
|3.11<br />
|-<br />
|Trp/8<br />
|1.54 <br />
|1.55<br />
|-<br />
|Trp/16<br />
|0.393 <br />
|0.409<br />
|-<br />
|Trp/32<br />
|0.013 <br />
|0.003<br />
|-<br />
| -H<br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
| His/2 <br />
|3.68 <br />
|3.84<br />
|-<br />
| His/4<br />
|2.07 <br />
|2.00<br />
|-<br />
|His/8<br />
|1.17 <br />
|0.97<br />
|-<br />
|His/16 <br />
|0.47 <br />
|0.432<br />
|-<br />
|His/32 <br />
|0.238 <br />
|0.257<br />
|-<br />
|SC (w/cells) <br />
|4.88 <br />
|4.91<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the [[Team:Buenos_Aires/Results/Model|mathematical model]], which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLU series<br />
|Images from TLU series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLU is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC).<br />
<br />
HLU and TLU are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:30:51Z<p>Abush84: /* Measurement of Trp in medium and Basal Production */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan secreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC).<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:26:00Z<p>Abush84: /* Coculture in Agar and Revertant mutation control */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|900px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated ~10^6 cells (lawn) or ~10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately, and to estimate the seed CFU (colony formin units) more precisely. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
<br />
Growth in coculture was puzzling, as it resulted in more colonies than the expected. If cooperation was effective, we expected to see as many colonies as "seed" cells, not more. Revertion of cells from the "lawn" doesn't explain the number of colonies either. Probably a combination of both these effects are taking place.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC).<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:17:54Z<p>Abush84: /* Coculture in Agar and Revertant mutation control */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain, and to asses if cross-feeding between a lawn of cells of one strain and colonies from and other strain is posible. <br />
<br />
We used petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10^6 cells (lawn) or 10^2 cells (seed) as shown by the following table (we considered OD600=1 represents 3*10^7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Lawn (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected, as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium is probably the same. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other's missing amino acids.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:13:25Z<p>Abush84: /* Results */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain. <br />
<br />
We used Petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10 6 cel or 102 cel as shown by the following table (we considered OD600: 1 represents 3.10 7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Grass (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in the table, we have a low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when interpreting the results from coculture growth after several days, given that the rate of revertants in liquid medium may as well be of the same. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other's missing aminoacids. This is encouraging since we now confirmed that each strain is able to see and relate with each other and therefore we have an open communication way over which to work and control with engineering.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:11:16Z<p>Abush84: /* Coculture in Agar and Revertant mutation control */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain. <br />
<br />
We used Petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10 6 cel or 102 cel as shown by the following table (we considered OD600: 1 represents 3.10 7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Grass (10^6 cells) <br />
! scope="row" style="background: #7ac5e8"|Seed (10^2 cells) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in Table, we have a very low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when seeing results from coculture growth after several days, given that the rate of revertants in liquid medium may as well be of the same numerical order. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other´s missing aminoacids. This is encouraging since we know confirmed that each strain is able to see and relate with each other and therefore we have an open communication way over which to work and control with engineering.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:08:19Z<p>Abush84: /* Auxotrophy confirmation */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophy check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in the SC plate (top left) and only 3154 (+H+T) grew in the -H-T plate (bottom right). In the -T plate (bottom left), only those strains able to synthesize T grew (3265 and 3154) and in the -H plate (top right) only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain. <br />
<br />
We used Petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10 6 cel or 102 cel as shown by the following table (we considered OD600: 1 represents 3.10 7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Grass (10 6 cel) <br />
! scope="row" style="background: #7ac5e8"|Seed (102 cel) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in Table, we have a very low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when seeing results from coculture growth after several days, given that the rate of revertants in liquid medium may as well be of the same numerical order. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other´s missing aminoacids. This is encouraging since we know confirmed that each strain is able to see and relate with each other and therefore we have an open communication way over which to work and control with engineering.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:04:26Z<p>Abush84: /* Fluorescence screening */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Measurment of strains fluorescence ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophies check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in Plate a and only 3154 (+H+T) grew in Plate c. In plate B, only those strains able to synthesize T grew (3265 and 3154) and in Plate d, only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain. <br />
<br />
We used Petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10 6 cel or 102 cel as shown by the following table (we considered OD600: 1 represents 3.10 7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Grass (10 6 cel) <br />
! scope="row" style="background: #7ac5e8"|Seed (102 cel) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in Table, we have a very low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when seeing results from coculture growth after several days, given that the rate of revertants in liquid medium may as well be of the same numerical order. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other´s missing aminoacids. This is encouraging since we know confirmed that each strain is able to see and relate with each other and therefore we have an open communication way over which to work and control with engineering.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/StrainsTeam:Buenos Aires/Results/Strains2012-09-27T03:03:33Z<p>Abush84: /* Fluorescence screening */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
<br />
<br />
== Description of strains ==<br />
<br />
Through our experiments we worked with the following strains kindly provided by [http://www.ifibyne.fcen.uba.ar/new/temas-de-investigacion/laboratorio-de-fisiologia-y-biologia-molecular-lfbm/biologia-de-sistemas/dr-alejandro-colman-lerner/ Alejandro Colman-Lerner's] Lab: <br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8" | Strain ID<br />
! scope="row" style="background: #7ac5e8" |Relevant Auxotrophies<br />
! scope="row" style="background: #7ac5e8" |Fluorescence<br />
! scope="row" style="background: #7ac5e8" |Used as<br />
|-<br />
|TCY 3043<br />
| style="text-align: center;" |(-H-T)<br />
|No fluorescence<br />
|Negative control<br />
|-<br />
|TCY 3190<br />
| style="text-align: center;" |(+H-T)<br />
|YFP + (Induced CFP)<br />
|For coculture<br />
|-<br />
|TCY 3265<br />
| style="text-align: center;" |(-H+T)<br />
|CFP<br />
|For coculture<br />
|-<br />
|TCY 3154<br />
| style="text-align: center;" |(+H+T)<br />
|CFP +(induced YFP)<br />
|Positive Control<br />
|}<br />
| rowspan="2" style="text-align: center;" |<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-figura1.jpg|300px]]<br />
|}<br />
|- valign="top"<br />
|<br />
<br />
In the table we can see Hystidine (H) and Tryptophane (T) auxotrophies per strain, type of fluorescence and description of most common utilization during the experiments.<br />
<br />
Nearly 15 other similar strains were evaluated and discarded due to several reasons (low screening potentiality; requirement of hormones for fluorescence induction; high reverting rate of auxotrophies, among others)<br />
|}<br />
<br />
== Fluorescence screening ==<br />
<br />
We measured Strains 3281 (YFP) and 3265 (CFP) and got a spectrum of each one prooving that these strains can be distinguished by their fluorescence in culture. <br />
<br />
'''Reference graph'''<br />
Image: YFP and CFP Emission and Absorption Spectra. Obtained from http://flowcyt.salk.edu/fluo.html<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Refefluro.png|450px]]<br />
|}<br />
<br />
<br />
'''Results'''<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-strains-Yfp.png|450px]]<br />
| align="center" | [[File:Bsas2012-strains-grafico2.png|500px]]<br />
|-<br />
|YFP Fluorescence Screening<br />
|CFP Fluorescence Screening<br />
|}<br />
<br />
When measuring YFP Strain 3281, we can see a clear peak around 530 while when measuring CFP Strain 3265, we can see a clear peak around 500, as expected.<br />
<br />
<br />
<br />
'''Discussion'''<br />
<br />
We were able to measure fluorescence in strains 3281 and 3265 using the spectrofluorometer. However, we considered it would not be precise enough for the purposes of measuring cocultures at different proportions. We also noticed a high background noise produced by dead yeast cells at high concentrations, which would make it possible to measure in this way only at a short range of OD while the culture is at exponential phase.<br />
<br />
== Screening of strain proportion ==<br />
<br />
A more precise way of measuring the proportion of the strains, is with a epifluorescence microscope.<br />
<br />
We mixed strains 3281 (expresses YFP) and 3265 (expresses CFP) in different proportions and analized the images obtained in the microscope, where we counted cells with different fluorescences. We also did a negative control with a non fluorescent strain (TCY 379). <br />
<br />
'''Description of Mixtures'''<br />
<br />
Mix 1: Negative Control<br />
<br />
Mix 2: 80% CFP; 20%YFP<br />
<br />
Mix 3: 60% CFP; 40%YFP<br />
<br />
Mix 4: 50% CFP; 50%YFP<br />
<br />
Mix 5: 40% CFP; 60%YFP<br />
<br />
Mix 6: 20% CFP; 80%YFP<br />
<br />
'''Results'''<br />
<br />
<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Montage-annotated.jpg|800px]]<br />
|-<br />
| style="text-align: center;" | Mixtures showing YFP and CFP fluorescence. <br />
|}<br />
<br />
<br />
<br />
As shown by images 1-6, cells showing different fluorescences can be count and distinguished from each other in a mixture of strains, and this could be used to measure strains proportion in a coculture. <br />
<br />
<br />
'''Counting of cells'''<br />
<br />
<br />
{| class="wikitable"<br />
|-<br />
|Fluorescence<br />
|Mix 1<br />
|Mix 2<br />
|Mix 3<br />
|Mix 4<br />
|Mix 5<br />
|Mix 6<br />
|-<br />
|YFP<br />
|0 <br />
|23 <br />
|67 <br />
|115 <br />
|135 <br />
|110<br />
|-<br />
|CFP<br />
|0* <br />
|235 <br />
|82 <br />
|107 <br />
|99 <br />
|78<br />
|}<br />
<br />
The table shows the number of cells counted by expression of fluorescence YFP and CFP in the different mixtures 1-6. I can be observed that the amount of cells is near the proportion stablished by OD measures when preparing the mixtures. This results confirms that epifluorescence measures are reliable and suitable for our research.<br />
<br />
== Auxotrophy confirmation ==<br />
<br />
<br />
Several times during the experiments we control and checked if the auxotrophies in the selected strain were functional by plating all of them in medium deficient in aminoacids (-H; -T; -H-T and control +H+T). <br />
We observed differential growth according to expected due to the description of each strain in point a)<br />
<br />
{| class="wikitable" <br />
|+ Auxotrophies check<br />
|-<br />
|[[File:Bsas2012-strains-figura3.jpg|300px]]<br />
|[[File:Bsas2012-strains-figura2.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium complete<br />
| style="text-align: center;" | Medium without H<br />
|}<br />
<br />
{| class="wikitable"<br />
|-<br />
|[[File:Bsas2012-strains-figura4.png|300px]]<br />
|[[File:Bsas2012-strains-figura5.png|300px]]<br />
|-<br />
| style="text-align: center;" | Medium without T<br />
| style="text-align: center;" | Medium without H and T<br />
|}<br />
<br />
We observed all the strains grew in Plate a and only 3154 (+H+T) grew in Plate c. In plate B, only those strains able to synthesize T grew (3265 and 3154) and in Plate d, only those able to produce H grew (3190 and 3154), as expected. This means our strains work according to their description. We did this several times during the months to check for reversions or contaminations.<br />
<br />
== Coculture in liquid medium ==<br />
<br />
We used for these experiment TCY3190(H+T-) and TCY3265(H-T+)<br />
Positive control: TCY3154 (H+T+) and negative control TCY3043(H-T-)<br />
<br />
==== At different initial OD and proportions ====<br />
<br />
Cultures were set at different initial concentrations (0.25, 0.1 and 0.01) and proportions (1:1; 1:9; 9:1). After 24 hs, we measured OD with the use of a spectrophotometer (two replicas) and we calculated the mean OD and a Growth factor (as Mean OD en time 1 over Initial OD time 0). <br />
<br />
<br />
{| class="wikitable"<br />
|+ Coculture at different initial OD and proportions (Days 0 and 1)<br />
! scope="row" style="background: #7ac5e8" | Coculture Proportion (H+T-):(H-T+) <br />
! scope="row" style="background: #7ac5e8" |Initial OD(t=0) <br />
! scope="row" style="background: #7ac5e8"|OD1 (t=1) <br />
! scope="row" style="background: #7ac5e8"|OD2 (t=1) <br />
! scope="row" style="background: #7ac5e8"|dilution used for measure t=1 <br />
! scope="row" style="background: #7ac5e8"|Mean OD <br />
! scope="row" style="background: #7ac5e8"|Growth Factor<br />
|-<br />
|01:01 <br />
|0,25 <br />
|0,32 <br />
|0,314 <br />
|10 <br />
|3,17 <br />
|12,68<br />
|-<br />
|09:01 <br />
|0,25 <br />
|0,148 <br />
|0,144 <br />
|10 <br />
|1,46 <br />
|5,84<br />
|-<br />
|01:09 <br />
|0,25 <br />
|0,138 <br />
|0,189 <br />
|10 <br />
|1,635 <br />
|6,54<br />
|-<br />
|01:01 <br />
|0,1 <br />
|0,109 <br />
|0,169 <br />
|10 <br />
|1,39 <br />
|13,9<br />
|-<br />
|09:01 <br />
|0,1 <br />
|0,04 <br />
|0,045 <br />
|10 <br />
|0,425 <br />
|4,25<br />
|-<br />
|01:09 <br />
|0,1 <br />
|0,067 <br />
|0,053 <br />
|10 <br />
|0,6 <br />
|6<br />
|-<br />
|01:01 <br />
|0,01 <br />
|0,067 <br />
|0,061 <br />
|1 <br />
|0,064 <br />
|6,4<br />
|-<br />
|09:01 <br />
|0,01 <br />
|0,056 <br />
|0,05 <br />
|1 <br />
|0,053 <br />
|5,3<br />
|-<br />
|01:09 <br />
|0,01 <br />
|0,074 <br />
|0,073 <br />
|1 <br />
|0,0735 <br />
|7,35<br />
|-<br />
|} <br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strain-Grafale2.png|800px]]<br />
|}<br />
<br />
<br />
{|<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Grafale1.png|400px]]<br />
|<!--column1-->[[File:Bsas2012-strains-grafico5.png|400px]]<br />
|}<br />
<br />
<br />
<br />
<br />
As shown in graphs there is a basal growth that does not depend on the initial OD or strain proportion, of a growth factor of 6 approximately.<br />
However we observed a much higher growth at the proportion 1:1 when the initial OD 0.25 and 0.1. Therefore we can assume that at these proportions there is a natural cooperation between the strains and that should be the level of growth that we would like to assess through our bioengineering. Besides we would like to be able in the future to tune the strains in order to be able to obtain in the proportions 9:1 and 1:9 similar results to those obtained in the 1:1, at our own will.<br />
<br />
==== At the same initial OD: 0.2, followed over time ====<br />
<br />
We set the same cultures and cocultures as in point i), but starting all of them at the same OD: 0.2 and we followed them over 2 days. At day 1 we took pictures of them and at day 2 we measured the final OD. <br />
<br />
{| align="center" <br />
|- valign="top"<br />
|<br />
{| class="wikitable"<br />
|+ Cultures set at initial OD: 0.2 and measured over time (Days 0 and 2)<br />
! scope="row" style="background: #7ac5e8"|Strain<br />
! scope="row" style="background: #7ac5e8"|Day 0 <br />
! scope="row" style="background: #7ac5e8"|Day 2<br />
|-<br />
|TCY 3190 (-H+T) <br />
|0,2<br />
|2,92<br />
|-<br />
|TCY 3265 (+H-T) <br />
|0,2<br />
|0,19<br />
|-<br />
|Coculture of strains (TCY 3190- TCY 3265) <br />
|0,2<br />
|2,76<br />
|-<br />
|Negative control (TCY 3043 / -H-T) <br />
|0,2<br />
|0,6<br />
|-<br />
|Positive Control (TCY 3154/ +H+T) <br />
|0,2<br />
|2,54<br />
|}<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-wednesday.png|500px]]<br />
|-<br />
|Picture: Day 1 after starting cultures, shows different OD reached by strains. <br />
|}<br />
<br />
We repeated this experiment 4 times with different modifications: increasing the amount of days for up to a week, measuring every 12 hs instead of every 24 hs and using different strains. However, bacterial contaminations and the high rate of revertants prevented us from getting to a valid results in those cases, whereas the experiment up to day 2 always worked correctly. This denotes that we should assess the problem of contamination (for example including ampicilin in the cultures) and revertant rate (revising the design of the experiment or looking for more stable strains) as the impossibility to go further than day 2 may put limitations to some applications of the Synthetic Community.<br />
<br />
== Coculture in Agar and Revertant mutation control ==<br />
<br />
<br />
Through this experiment we aim to quantify the rate of revertants of each strain. <br />
<br />
We used Petri dishes with agar medium with (+) and without (-) Trp and His as shown in the following table.<br />
<br />
We started a culture of each strain in synthetic complete medium, measured its OD 24 hs after the culture initiated, replaced the synthetic complete medium for one lacking both H and T (to avoid residual growth) and plated 10 6 cel or 102 cel as shown by the following table (we considered OD600: 1 represents 3.10 7 cells). <br />
At the same time, 3 controls (one for each strain) were carried in YPD complete medium to check the viability of each strain separately. <br />
<br />
{| class="wikitable"<br />
! scope="row" style="background: #7ac5e8"|Medium H<br />
! scope="row" style="background: #7ac5e8"|Medium T<br />
! scope="row" style="background: #7ac5e8"|Grass (10 6 cel) <br />
! scope="row" style="background: #7ac5e8"|Seed (102 cel) <br />
! scope="row" style="background: #7ac5e8"|Description of experiment <br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 1<br />
! scope="row" style="background: #7ac5e8"|Results after 3 days - Replica 2<br />
|-<br />
|(-) <br />
|(+) <br />
|(-) <br />
|Strain –H+T <br />
|Control of His revertants <br />
|7 <br />
|7<br />
|-<br />
|-<br />
|(+)<br />
|(-)<br />
|(-)<br />
|Strain +H-T <br />
|Control of Trp revertants <br />
|2 <br />
|7<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain +H-T<br />
|Strain –H+T<br />
|Coculture; we expect to see natural cooperation<br />
|960<br />
|800<br />
|-<br />
|(-)<br />
|(-)<br />
|Strain –H+T<br />
|Strain +H-T<br />
|Coculture; we expect to see natural cooperation<br />
|500<br />
|712<br />
|-<br />
|(-)<br />
|(-)<br />
|(-)<br />
|Strain +H+T<br />
|Viability of yeasts in medium<br />
|171<br />
|(-)<br />
|}<br />
<br />
'''Table: Shows description of each plate content and results in number of colonies counted by plate at day 3. YPD control results plates are not shown in the table'''. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-placas2.jpg|300px]]<br />
|<!--column1-->[[File:Bsas2012-strains-placas1.jpg|300px]]<br />
|-<br />
|Petri Dishes<br />
| With marks of the counting of colonies<br />
|}<br />
<br />
<br />
==== Results ====<br />
The viability of the strains was high as expected as well as the viability of a control positive strain in the –H-T medium. <br />
As shown in Table, we have a very low, but existent, number of revertants from both his and trp auxotrophy strains. This number should be taken into account when seeing results from coculture growth after several days, given that the rate of revertants in liquid medium may as well be of the same numerical order. <br />
Growth in coculture was high and near to the number we expected as result of natural cooperation, which confirms that there is a natural way in which the strains cooperate by sharing each other´s missing aminoacids. This is encouraging since we know confirmed that each strain is able to see and relate with each other and therefore we have an open communication way over which to work and control with engineering.<br />
<br />
== Measurement of Trp in medium and Basal Production ==<br />
<br />
To check the efectiveness of our biobricks, we must first determine the ammount of tryptophan excreted by natural strains to the medium, so we can compare. With that end in mind, we designed a protocol for measurement of tryptophan in medium, based in its fluorescense at 350nm, when excited with 295nm light.<br />
As a previous step, we checked that none of the other aminoacids used in the medium interferes, by graphically comparing the spectres for uncomplemented medium and medium complemented with leucine, uracile and histidine, at an appropiate range.<br />
<br />
To determine Trp concentration, we must first have a way to transform our readings (intensity) to a more useful output, so we made a calibration curve, through serialized 1:2 dilutions of our medium, which Trp's concentration is 50mg/mL, until approximately constant intensity.<br />
<br />
The procedure to measure secretion rates will be growing the strain from a known OD in exponential growth phase in -T medium and plotting it's OD over time, spin-drying at time=t, retrieving the supernatant's Trp concentration and dividing it by the integral of OD vs. time between time=0 and time=t, so we get to a rate which will be proportional to the number of cells in the culture, which means we can actually compare between different strains. Since our medium is free from Trp, all of it should come from within the cells, and if the culture is growing at exponential rates, lysis should be negligible, so the only explanation would be cells exporting their own Trp.<br />
<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-Trpmario.png]]<br />
|-<br />
|Graph:Tryptophan calibration curve<br />
|}<br />
<br />
<br />
<br />
==== Results ====<br />
<br />
As can be seen from the graph the screening of the concentration of the Trp in medium describes an almost lineal function. Through this experiment we can be sure that we would be able to measure increase of Trp in medium as it is exported from the cells, within the biological range of export.<br />
The sensitivity of this method seems to be enough to detect concentrations as low as ~0.02mg/mL, and as high as 50mg/mL, maybe more. Since our medium is 50mg/mL, we assume that's the saturation point of the curve. If we get bigger intensities than the one corresponding to it, we will dilute the sample.<br />
<br />
Because of time constraints, we haven't been able to check the method with either our designed strains nor the non-exporting ones.<br />
<br />
== Growth dependence on the Trp and His concentrations ==<br />
<br />
A important thing to characterize of the system is the dependence of the growth rate of the culture with the concentration of the crossfeeding aminoacids, tryptophane (Trp) and histidine (His). To do this we measured the final OD after an overnight growth in medium with different concentrations of Trp and His. <br />
<br />
We used strain ACL-379, that is auxotroph for both Trp and His. <br />
We prepared serial dilutions of SC medium in –T and –H medium, therefore creating two curves: one with decreasing concentrations of Trp and the other with decreasing concentrations of His. <br />
We then inoculated equal amounts of ACL-379 in each tube and incubated them overnight at 30°C with agitation. We took a picture of each tube and measured the OD600 reached by each culture.<br />
<br />
{| class="wikitable"<br />
|+Growth of ACL-379 as a function of Trp and His concentration<br />
! scope="row" style="background: #7ac5e8"|Medium<br />
! scope="row" style="background: #7ac5e8"|OD Replica 1<br />
! scope="row" style="background: #7ac5e8"|OD Replica 2<br />
|-<br />
|SC<br />
|0,001<br />
|(-0,0036)<br />
|-<br />
|HLC <br />
|(-0,003)<br />
|(-0,019)<br />
|-<br />
|S1 HLC (1:10) <br />
|0,256 <br />
|0,217<br />
|-<br />
|S2 HLC (1:10) <br />
|0,301 <br />
|0,311<br />
|-<br />
|S3 HLC (1:10) <br />
|0,154 <br />
|0,155<br />
|-<br />
|S4 HLC <br />
|0,393 <br />
|0,409<br />
|-<br />
|S5 HLC <br />
|0,013 <br />
|0,003<br />
|-<br />
|TLC <br />
|(-0,008) <br />
|(-0,012)<br />
|-<br />
|S1 TLC (1:10) <br />
|0,368 <br />
|0,384<br />
|-<br />
|S2 TLC (1:10) <br />
|0,207 <br />
|0,2<br />
|-<br />
|S3 TLC (1:10) <br />
|0,117 <br />
|0,097<br />
|-<br />
|S4 TLC <br />
|0,47 <br />
|0,432<br />
|-<br />
|S5 TLC <br />
|0,238 <br />
|0,257<br />
|-<br />
|HTLC (1:10) <br />
|0,488 <br />
|0,491<br />
|} <br />
<br />
==== Results ====<br />
As expected the growth has a sigmoidal relationship with the concentration of Trp and His, when plotted in semilogarithmic scale. We call EC50 the effective concentration of each aminoacid at which the culture reaches 50% of the maximal growth. We considered these values as proxies of the Khis and Ktrp parameters of the mathematical model, which can be used to estimate the secretion rate of each aminoacid needed to get effective crossfeeding. <br />
<br />
These results can also be observed by comparison of images that show the tubes at different OD. <br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-strains-alan1.png|500px]]<br />
|<!--column2-->[[File:Bsas2012-strains-ultima.jpg|500px]] <br />
|-<br />
|Images from HLC series<br />
|Images from TLC series<br />
|}<br />
<br />
Notes: <br />
SC: Synthetic complete medium with all the aminoacids. It was used as a blank for the spectrofluorometer.<br />
<br />
HTLC is the culture in the medium with all the required aminoacids.<br />
<br />
S(Number) are the serial dilutions of HTLC with medium that lacks Histidine (HLC) and Tryptophane (TLC.<br />
<br />
HLC and TLC are mediums without cells.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb2Team:Buenos Aires/Results/Bb22012-09-27T02:55:24Z<p>Abush84: /* Bacteria Exportable His-rich peptide Generator */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Why this biobrick? =<br />
<br />
We were having some '''custom related''' issues with the synthesized dna we ordered (see [[Team:Buenos_Aires/Results/Bb1 | aa-rich export devices]]), so we started designing '''a biobrick that could be used for the same purpose, but that uses just parts included in the 2012 kit'''. Yes, you are correct: '''a PlanB'''!<br />
<br />
Although we did not work in the lab with this ''planB'' biobrick - because fortunately the synthesized dna made it through customs (although very late) - we are including it in the registry and the wiki for documentary purposes only. Just follow this link <partinfo>BBa_K792014</partinfo> to access its entry in the registry.<br />
<br />
= Bacteria Exportable His-rich peptide Generator =<br />
<br />
As mentioned before, for this biobrick we had to use solely parts that came in the iGEM Kit 2012 Spring distribution, and that could work for the same purposes that our main biobrick, which is mainly the export of aminoacids. As there are much more parts for E. coli than for yeast, we decided to build a bacterial device as a "proof of principle" that amino acid secretion can regulated in this way. <br />
<br />
We designed a plausible construct that could work for the export of ''His'' using 5 parts of the registry, but we didn't find enough parts in order to design a simmilar one for the export of ''Trp''. <br />
<br />
Furthermore, the binding and preparation of this device is much more complex and has many more steps than what our main biobrick would require to work. Therefore, we conclude that our main biobrick is an important contribution to the registry part, given that it allows the export of aminoacids His and Trp to be enhanced with the use of only one part, without the need of the many steps that we describe in this section and consequently reducing the risk of failure and errors.<br />
<br />
<!--<br />
==== Aim ====<br />
<br />
* To create a biobrick that would enhance Histidine secretion in E. coli using standard parts of the registry as a proof that one can increase the production and secretion of an aminoacid and its measurement in the culture medium. <br />
* To learn how to use and merge standard parts from the registry provided at the iGEM Kit. <br />
* To characterize the functioning of existent parts in the registry and new combinations of them, therefore contributing the improvement of the iGEM record.<br />
* To proove that our main biobricks, devices 1 and 3 for the export of His, are an important contribution to iGEM, given that they are capable of His export with far less steps involved. <br />
* To proove that our main biobricks, devices 2 and 4 for the export of Trp, are an important contribution to iGEM, given that there are was no biobrick with such function available.<br />
--><br />
<br />
== Device final structure ==<br />
<br />
{|<br />
|<br />
Our device is composed by:<br />
* '''Promoter''' (<partinfo>BBa_J04500</partinfo>)<br />
* '''Ribosome binding site''' (<partinfo>BBa_B0034</partinfo>)<br />
* '''Peptide signal''' or secretion tag so that the aminoacid is exported out of the cell (<partinfo>BBa_K125310</partinfo>)<br />
* '''Payload''', a ''histidine tag'' repeated several times in order to have a long peptide enriched in this aa (<partinfo>BBa_K133035</partinfo>)<br />
* '''Terminator''' (<partinfo>BBa_B0024</partinfo>)<br />
|<br />
{|width="100%"<br />
|+ '''Structure of the device'''<br />
|align="center" | [[File:Bsas2012-Bb5.png|500px]]<br />
|}<br />
|}<br />
<br />
This construct should be able to export '''Histidine''', for sure in ''Cyanobacterium'' and to be tested in ''E.Coli'' and ''S. cereviciae''.<br />
'''More details about the design process (and choices made) can be found in the following sections'''.<br />
<br />
== Design process ==<br />
<br />
=== Promoter ===<br />
<br />
We found many usable parts to use as promoters. We found Promoters + RBS ideal for our purposes, in order to economize ligation steps. <br />
We considered using two parts: <br />
<br />
# Promoter + RBS (<partinfo>BBa_K206015</partinfo>), a strongest constitutive promoter in J23100 family (J23100) + mid-strength RBS from the community collection (B0030, 0.6. It looks like a reliable sequence but it has not been tested according to the registry. <br />
# Inducible Promoter (IPTG) + RBS (Strong) (<partinfo>BBa_J04500</partinfo>). This part has been tested and according to the registry it works well. <br />
<br />
We finally decided to use the second opton (<partinfo>BBa_J04500</partinfo>) in order to make our system plausible of regulation through IPTG. This kind of regulation could also have been implemented in our main biobricks, for the same purposes or making the system more flexible.<br />
<br />
=== Signal Peptide ===<br />
<br />
Unfortunately, we found very few signal peptide biobrick options, solely two and tested in Cyanobacterium. Our two options were:<br />
<br />
# pilA1 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125300</partinfo><br />
# slr2016 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125310</partinfo><br />
<br />
These parts are only partically confirmed and optimized for working in Cyanobacterium, not E. coli or Yeast. We could use any of them in order to test them but not having any E.coli or yeast optimized signal peptide available at the registry is a critical obstacle in the project.<br />
<br />
We would use the second one: <partinfo>BBa_K125310</partinfo>.<br />
<br />
=== Payload ===<br />
<br />
The only option available in iGEM Kit Spring distribution 2012 was Methionine + His Affinity Tag x 6: <partinfo>BBa_K133035</partinfo>. This part is only partially confirmed. We would put this part 3 times in a row in order to have a larger peptide - so it would take 3 ligation steps to obtain a larger peptide enriched Histidine, whereas in our main biobrick 1 and 3, it already comes in the same construct.<br />
<br />
=== Terminator ===<br />
{|<br />
|- valign="top"<br />
| width="70%" | <br />
<br />
There are several options for terminators, but we would use a double terminator in order to be sure that it works. We choose the double terminator <partinfo>BBa_B0024</partinfo>. This sequence has a double terminator in several of reading frames, which could be very useful. <br />
| align="center" | <br />
{| class="wikitable" style="width:200px"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Translate.png|200px]]<br />
|-<br />
|Different Reading Frames of BBa_B0024. Frames 1 and 3 are useful as double terminators. <br />
|}<br />
|}<br />
<br />
== Assembly method == <br />
<br />
All the chosen parts are compatible with '''RFC 21 Standard''', which is an in frame assembly method and would be our choice for assembling these parts.<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Finalbb5.JPG|500px]]<br />
|-<br />
|Picture: Final sequence with prefix, suffix and scars assembled with RFC 21 Standard method. <br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb2Team:Buenos Aires/Results/Bb22012-09-27T02:51:09Z<p>Abush84: /* Why this biobrick? */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Why this biobrick? =<br />
<br />
We were having some '''custom related''' issues with the synthesized dna we ordered (see [[Team:Buenos_Aires/Results/Bb1 | aa-rich export devices]]), so we started designing '''a biobrick that could be used for the same purpose, but that uses just parts included in the 2012 kit'''. Yes, you are correct: '''a PlanB'''!<br />
<br />
Although we did not work in the lab with this ''planB'' biobrick - because fortunately the synthesized dna made it through customs (although very late) - we are including it in the registry and the wiki for documentary purposes only. Just follow this link <partinfo>BBa_K792014</partinfo> to access its entry in the registry.<br />
<br />
= Bacteria Exportable His-rich peptide Generator =<br />
<br />
As mentioned before, for this biobrick we had to use solely parts that came in the iGEM Kit 2012 Spring distribution, and that could work for the same purposes that our main biobrick, which is mainly the export of aminoacids. <br />
<br />
We designed a plausible construct that could work for the export of ''His'' using 5 parts of the registry, but we didnt find enough parts in order to design a simmilar one for the export of ''Trp''. <br />
<br />
Furthermore, the binding and preparation of this device is much more complex and has many more steps than what our main biobrick would require to work. Therefore, we conclude that our main biobrick is an important contribution to the registry part, given that it allows the export of aminoacids His and Trp to be enhanced with the use of only one part, without the need of the many steps that we describe in this section and consequently reducing the risk of failure and errors.<br />
<br />
<!--<br />
==== Aim ====<br />
<br />
* To create a biobrick that would enhance Histidine secretion in E. coli using standard parts of the registry as a proof that one can increase the production and secretion of an aminoacid and its measurement in the culture medium. <br />
* To learn how to use and merge standard parts from the registry provided at the iGEM Kit. <br />
* To characterize the functioning of existent parts in the registry and new combinations of them, therefore contributing the improvement of the iGEM record.<br />
* To proove that our main biobricks, devices 1 and 3 for the export of His, are an important contribution to iGEM, given that they are capable of His export with far less steps involved. <br />
* To proove that our main biobricks, devices 2 and 4 for the export of Trp, are an important contribution to iGEM, given that there are was no biobrick with such function available.<br />
--><br />
<br />
== Device final structure ==<br />
<br />
{|<br />
|<br />
Our device is composed by:<br />
* '''Promoter''' (<partinfo>BBa_J04500</partinfo>)<br />
* '''Ribosome binding site''' (<partinfo>BBa_B0034</partinfo>)<br />
* '''Peptide signal''' or secretion tag so that the aminoacid is exported out of the cell (<partinfo>BBa_K125310</partinfo>)<br />
* '''Payload''', a ''histidine tag'' repeated several times in order to have a long peptide enriched in this aa (<partinfo>BBa_K133035</partinfo>)<br />
* '''Terminator''' (<partinfo>BBa_B0024</partinfo>)<br />
|<br />
{|width="100%"<br />
|+ '''Structure of the device'''<br />
|align="center" | [[File:Bsas2012-Bb5.png|500px]]<br />
|}<br />
|}<br />
<br />
This construct should be able to export '''Histidine''', for sure in ''Cyanobacterium'' and to be tested in ''E.Coli'' and ''S. cereviciae''.<br />
'''More details about the design process (and choices made) can be found in the following sections'''.<br />
<br />
== Design process ==<br />
<br />
=== Promoter ===<br />
<br />
We found many usable parts to use as promoters. We found Promoters + RBS ideal for our purposes, in order to economize ligation steps. <br />
We considered using two parts: <br />
<br />
# Promoter + RBS (<partinfo>BBa_K206015</partinfo>), a strongest constitutive promoter in J23100 family (J23100) + mid-strength RBS from the community collection (B0030, 0.6. It looks like a reliable sequence but it has not been tested according to the registry. <br />
# Inducible Promoter (IPTG) + RBS (Strong) (<partinfo>BBa_J04500</partinfo>). This part has been tested and according to the registry it works well. <br />
<br />
We finally decided to use the second opton (<partinfo>BBa_J04500</partinfo>) in order to make our system plausible of regulation through IPTG. This kind of regulation could also have been implemented in our main biobricks, for the same purposes or making the system more flexible.<br />
<br />
=== Signal Peptide ===<br />
<br />
Unfortunately, we found very few signal peptide biobrick options, solely two and tested in Cyanobacterium. Our two options were:<br />
<br />
# pilA1 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125300</partinfo><br />
# slr2016 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125310</partinfo><br />
<br />
These parts are only partically confirmed and optimized for working in Cyanobacterium, not E. coli or Yeast. We could use any of them in order to test them but not having any E.coli or yeast optimized signal peptide available at the registry is a critical obstacle in the project.<br />
<br />
We would use the second one: <partinfo>BBa_K125310</partinfo>.<br />
<br />
=== Payload ===<br />
<br />
The only option available in iGEM Kit Spring distribution 2012 was Methionine + His Affinity Tag x 6: <partinfo>BBa_K133035</partinfo>. This part is only partially confirmed. We would put this part 3 times in a row in order to have a larger peptide - so it would take 3 ligation steps to obtain a larger peptide enriched Histidine, whereas in our main biobrick 1 and 3, it already comes in the same construct.<br />
<br />
=== Terminator ===<br />
{|<br />
|- valign="top"<br />
| width="70%" | <br />
<br />
There are several options for terminators, but we would use a double terminator in order to be sure that it works. We choose the double terminator <partinfo>BBa_B0024</partinfo>. This sequence has a double terminator in several of reading frames, which could be very useful. <br />
| align="center" | <br />
{| class="wikitable" style="width:200px"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Translate.png|200px]]<br />
|-<br />
|Different Reading Frames of BBa_B0024. Frames 1 and 3 are useful as double terminators. <br />
|}<br />
|}<br />
<br />
== Assembly method == <br />
<br />
All the chosen parts are compatible with '''RFC 21 Standard''', which is an in frame assembly method and would be our choice for assembling these parts.<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Finalbb5.JPG|500px]]<br />
|-<br />
|Picture: Final sequence with prefix, suffix and scars assembled with RFC 21 Standard method. <br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb2Team:Buenos Aires/Results/Bb22012-09-27T02:49:50Z<p>Abush84: /* Why this biobrick? */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Why this biobrick? =<br />
<br />
We were having some '''custom related''' issues with the synthesized dna we ordered (see [[Team:Buenos_Aires:Results:Bb1 | aa-rich export devices]]), so we started designing '''a biobrick that could be used for the same purpose, but that uses just parts included in the 2012 kit'''. Yes, you are correct: '''a PlanB'''!<br />
<br />
Although we did not work in the lab with this ''planB'' biobrick - because fortunately the synthesized dna made it through customs (although very late) - we are including it in the registry and the wiki for documentary purposes only. Just follow this link <partinfo>BBa_K792014</partinfo> to access its entry in the registry.<br />
<br />
= Bacteria Exportable His-rich peptide Generator =<br />
<br />
As mentioned before, for this biobrick we had to use solely parts that came in the iGEM Kit 2012 Spring distribution, and that could work for the same purposes that our main biobrick, which is mainly the export of aminoacids. <br />
<br />
We designed a plausible construct that could work for the export of ''His'' using 5 parts of the registry, but we didnt find enough parts in order to design a simmilar one for the export of ''Trp''. <br />
<br />
Furthermore, the binding and preparation of this device is much more complex and has many more steps than what our main biobrick would require to work. Therefore, we conclude that our main biobrick is an important contribution to the registry part, given that it allows the export of aminoacids His and Trp to be enhanced with the use of only one part, without the need of the many steps that we describe in this section and consequently reducing the risk of failure and errors.<br />
<br />
<!--<br />
==== Aim ====<br />
<br />
* To create a biobrick that would enhance Histidine secretion in E. coli using standard parts of the registry as a proof that one can increase the production and secretion of an aminoacid and its measurement in the culture medium. <br />
* To learn how to use and merge standard parts from the registry provided at the iGEM Kit. <br />
* To characterize the functioning of existent parts in the registry and new combinations of them, therefore contributing the improvement of the iGEM record.<br />
* To proove that our main biobricks, devices 1 and 3 for the export of His, are an important contribution to iGEM, given that they are capable of His export with far less steps involved. <br />
* To proove that our main biobricks, devices 2 and 4 for the export of Trp, are an important contribution to iGEM, given that there are was no biobrick with such function available.<br />
--><br />
<br />
== Device final structure ==<br />
<br />
{|<br />
|<br />
Our device is composed by:<br />
* '''Promoter''' (<partinfo>BBa_J04500</partinfo>)<br />
* '''Ribosome binding site''' (<partinfo>BBa_B0034</partinfo>)<br />
* '''Peptide signal''' or secretion tag so that the aminoacid is exported out of the cell (<partinfo>BBa_K125310</partinfo>)<br />
* '''Payload''', a ''histidine tag'' repeated several times in order to have a long peptide enriched in this aa (<partinfo>BBa_K133035</partinfo>)<br />
* '''Terminator''' (<partinfo>BBa_B0024</partinfo>)<br />
|<br />
{|width="100%"<br />
|+ '''Structure of the device'''<br />
|align="center" | [[File:Bsas2012-Bb5.png|500px]]<br />
|}<br />
|}<br />
<br />
This construct should be able to export '''Histidine''', for sure in ''Cyanobacterium'' and to be tested in ''E.Coli'' and ''S. cereviciae''.<br />
'''More details about the design process (and choices made) can be found in the following sections'''.<br />
<br />
== Design process ==<br />
<br />
=== Promoter ===<br />
<br />
We found many usable parts to use as promoters. We found Promoters + RBS ideal for our purposes, in order to economize ligation steps. <br />
We considered using two parts: <br />
<br />
# Promoter + RBS (<partinfo>BBa_K206015</partinfo>), a strongest constitutive promoter in J23100 family (J23100) + mid-strength RBS from the community collection (B0030, 0.6. It looks like a reliable sequence but it has not been tested according to the registry. <br />
# Inducible Promoter (IPTG) + RBS (Strong) (<partinfo>BBa_J04500</partinfo>). This part has been tested and according to the registry it works well. <br />
<br />
We finally decided to use the second opton (<partinfo>BBa_J04500</partinfo>) in order to make our system plausible of regulation through IPTG. This kind of regulation could also have been implemented in our main biobricks, for the same purposes or making the system more flexible.<br />
<br />
=== Signal Peptide ===<br />
<br />
Unfortunately, we found very few signal peptide biobrick options, solely two and tested in Cyanobacterium. Our two options were:<br />
<br />
# pilA1 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125300</partinfo><br />
# slr2016 signal sequence from cyanobacterium Synechocystis; secretes protein: <partinfo>BBa_K125310</partinfo><br />
<br />
These parts are only partically confirmed and optimized for working in Cyanobacterium, not E. coli or Yeast. We could use any of them in order to test them but not having any E.coli or yeast optimized signal peptide available at the registry is a critical obstacle in the project.<br />
<br />
We would use the second one: <partinfo>BBa_K125310</partinfo>.<br />
<br />
=== Payload ===<br />
<br />
The only option available in iGEM Kit Spring distribution 2012 was Methionine + His Affinity Tag x 6: <partinfo>BBa_K133035</partinfo>. This part is only partially confirmed. We would put this part 3 times in a row in order to have a larger peptide - so it would take 3 ligation steps to obtain a larger peptide enriched Histidine, whereas in our main biobrick 1 and 3, it already comes in the same construct.<br />
<br />
=== Terminator ===<br />
{|<br />
|- valign="top"<br />
| width="70%" | <br />
<br />
There are several options for terminators, but we would use a double terminator in order to be sure that it works. We choose the double terminator <partinfo>BBa_B0024</partinfo>. This sequence has a double terminator in several of reading frames, which could be very useful. <br />
| align="center" | <br />
{| class="wikitable" style="width:200px"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Translate.png|200px]]<br />
|-<br />
|Different Reading Frames of BBa_B0024. Frames 1 and 3 are useful as double terminators. <br />
|}<br />
|}<br />
<br />
== Assembly method == <br />
<br />
All the chosen parts are compatible with '''RFC 21 Standard''', which is an in frame assembly method and would be our choice for assembling these parts.<br />
<br />
{| class="wikitable" border="1"<br />
|-<br />
|<!--column1-->[[File:Bsas2012-Finalbb5.JPG|500px]]<br />
|-<br />
|Picture: Final sequence with prefix, suffix and scars assembled with RFC 21 Standard method. <br />
|}</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:48:39Z<p>Abush84: /* References */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2&mu; ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
<br />
=== References ===<br />
<br />
*Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
*Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
*Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
*Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
*Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:47:22Z<p>Abush84: /* Considering Yeast Expression Plasmids */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2&mu; ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
<br />
=== References ===<br />
<br />
Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:46:20Z<p>Abush84: /* References */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2 ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
<br />
=== References ===<br />
<br />
Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:45:33Z<p>Abush84: /* Additional notes */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2 ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
<br />
=== References ===<br />
<br />
Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.<br />
<br />
=== References ===<br />
<br />
Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:42:06Z<p>Abush84: /* Additional notes */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2 ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
=== Additional notes ===<br />
<br />
When we order the construct we will probably have to specify in which vector we want it shipped. RS3 (and perhaps RS1) could be changed depending on this vector. For example we could order the gene in pBluescript, in which case it might be convenient to make RS3 -> SacI. For other plasmids other RS might be needed, but if possible it would be convenient to retain RS1 -> XhoI that allows us to clone the gene into pBluescript as described above.<br />
<br />
=== References ===<br />
<br />
Clements, J. M., G. H. Catlin, et al. (1991). "Secretion of human epidermal growth factor from Saccharomyces cerevisiae using synthetic leader sequences." Gene 106(2): 267-271.<br />
Cochran, A. G., N. J. Skelton, et al. (2001). "Tryptophan zippers: stable, monomeric beta -hairpins." Proc Natl Acad Sci U S A 98(10): 5578-5583.<br />
Derossi, D., G. Chassaing, et al. (1998). "Trojan peptides: the penetratin system for intracellular delivery." Trends Cell Biol 8(2): 84-87.<br />
Jones, S. W., R. Christison, et al. (2005). "Characterisation of cell-penetrating peptide-mediated peptide delivery." Br J Pharmacol 145(8): 1093-1102.<br />
Waters, M. G., E. A. Evans, et al. (1988). "Prepro-alpha-factor has a cleavable signal sequence." J Biol Chem 263(13): 6209-6214.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Results/Bb1Team:Buenos Aires/Results/Bb12012-09-27T02:41:07Z<p>Abush84: /* Selection of restriction sites */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= PoPS -> His/Trp rich peptide export devices =<br />
<br />
In order for the cross-feeding scheme to work we need each strain to export the amino acid they produce (either Histidine or Tryptophan). To achieve this we created a devices design to secrete to the medium an His (or Trp) rich peptides.<br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Crossfeeding-device-design_v01.jpg]]<br />
|-<br />
| align="center" | '''Basic DNA structure of the devices with their constitutive parts'''<br />
|}<br />
<br />
<br />
* '''Kozak''' consensus sequence for initiation of translation.<br />
* '''Signal peptide''' that targets the product of the gene for secretion.<br />
* '''Trojan peptide''' to increase internalization in target cell.<br />
* '''Payload''': this is the exported amino acid rich domain of the protein.<br />
<br />
The input of the devices are ''PoPS'' and the output is secreted amino acids, so the devices are ''PoPS -> exported AA'' transducers. In principle any PoPS generating part can be used.<br />
<br />
<br />
We built 2 '''His-export devices''', and 2 '''Trp-export devices''':<br />
* His-export I (<partinfo>BBa_K792009</partinfo>)<br />
* Trp export I (<partinfo>BBa_K792010</partinfo>)<br />
* His-export II (<partinfo>BBa_K792011</partinfo>)<br />
* Trp export II (<partinfo>BBa_K792012</partinfo>)<br />
<br />
<br />
To achive this, we had to create several '''new basic biobricks''':<br />
* Kozak sequence from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792001</partinfo>)<br />
* Secretion tag from yeast α-factor mating pheromone (MFα1) (<partinfo>BBa_K792002</partinfo>)<br />
* HIV TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
* Polyarginine trojan peptide (<partinfo>BBa_K792004</partinfo>)<br />
* PolyHa, a Histidine rich peptide (His-Tag) (<partinfo>BBa_K792005</partinfo>)<br />
* TrpZipper2, a Tryptophan rich peptide water soluble and monomeric (<partinfo>BBa_K792006</partinfo>)<br />
* PolyHb, a stable Histidine rich peptide designed by us (<partinfo>BBa_K792007</partinfo>)<br />
* PolyWb, a stable Tryptophan rich peptide designed by us(<partinfo>BBa_K792008</partinfo>)<br />
<br />
Details about how we create these new basic biobricks can be found in the next sections. More details can be found in their registry entries also.<br />
<br />
<br />
The next table summaries each ''export device'' composition.<br />
<br />
{| class="wikitable" width=80%<br />
|-<br />
! scope="row" style="background: #7ac5e8"| '''Device'''<br />
! scope="row" style="background: #7ac5e8"| '''Kozak'''<br />
! scope="row" style="background: #7ac5e8"| '''Signal peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Trojan peptide'''<br />
! scope="row" style="background: #7ac5e8"| '''Payload'''<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export I (<partinfo>BBa_K792009</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| PoliHa (HisTag) (<partinfo>BBa_K792005</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export I (<partinfo>BBa_K792010</partinfo>)<br />
| MFα1 [-12;6] (<partinfo>BBa_K792001</partinfo>)<br />
| MFα1 secretion tag (<partinfo>BBa_K792002</partinfo>)<br />
| TAT penetratin (<partinfo>BBa_K792003</partinfo>)<br />
| TrpZipper2 (<partinfo>BBa_K792006</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| His-export II (<partinfo>BBa_K792011</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyHb (<partinfo>BBa_K792007</partinfo>)<br />
|-<br />
! scope="row" style="background: #CCCCCC"| Trp-export II (<partinfo>BBa_K792012</partinfo>)<br />
| <partinfo>BBa_J63003</partinfo><br />
| <partinfo>BBa_K416003</partinfo><br />
| Polyarginine (<partinfo>BBa_K792004</partinfo>)<br />
| PolyWb (<partinfo>BBa_K792008</partinfo>)<br />
|}<br />
<br />
== Kozak Sequence ==<br />
{|<br />
| style="width: 75%" | The Kozak sequence is the eukaryotic analog to the bacterial RBS, it is required for efficient initiation of translation. There is only one yeast Kozak sequence in the registry (part [http://partsregistry.org/Part:BBa_J63003 BBa_J63003], distributed in the [http://partsregistry.org/assembly/libraries.cgi?id=42 2012 kit]). Note that this sequence codes for a glutamic acid (E) after the start codon. <br />
<br />
We decided to create a new part (<partinfo>BBa_K792001</partinfo>) using the 5’UTR of the [http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1] gene of yeast, partly because we used the signal peptide from this gene (see below). This gene is efficiently translated in yeast, and therefore it stands to reason that translation is efficiently initiated on its mRNA. <br />
<br />
| style="width: 25%" align="center" |<br />
<br />
[[File:Bsas2012-Kozak-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Kozak consensus parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" |DNA Sequence<br />
|-<br />
|[http://partsregistry.org/Part:BBa_J63003 BBa_J63003]<br />
|CCCGCCGCCACCATGGAG<br />
|-<br />
|<partinfo>BBa_K792001</partinfo><br />
|ACGATTAAAAGAATGAGA<br />
|}<br />
<br />
<br />
|}<br />
<br />
== Signal Peptide ==<br />
<br />
{|<br />
| style="width: 75%" | The signal peptides target proteins for secretion, effectively exporting the payload. This peptides are cleaved once the protein is in the lumen of the ER, so they won't have any further relevance.<br />
<br />
<br />
Part <partinfo>BBa_K416003</partinfo> of the registry codes for a yeast signal peptide, based on [Clements 1991]. As an alternative we designed a second part (<partinfo>BBa_K792002</partinfo>) coding for another signal papetide, from the yeast &alpha;-mating factor gene ([http://www.yeastgenome.org/cgi-bin/locus.fpl?dbid=S000006108 MF&alpha;1]) [Waters et al 1987]. This part is likely to work well when combined with the Kozak sequence from the same gene (<partinfo>BBa_K792001</partinfo>, see above), as the natural 5' end of the MF&alpha;1 transcript is reconstituted. Also, because it is a yeast gene, it can be used as is, without any optimization. <br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Signal-box.jpg | 200px]]<br />
<br />
{| class="wikitable"<br />
|+ Secretion tag parts<br />
! scope="row" style="background: #7ac5e8" |Part<br />
! scope="row" style="background: #7ac5e8" | <br />
|-<br />
|<partinfo>BBa_K416003</partinfo><br />
| already in registry<br />
|-<br />
|<partinfo>BBa_K792002</partinfo><br />
| our contribution<br />
|}<br />
<br />
|}<br />
<br />
== Trojan peptide ==<br />
<br />
{|<br />
|-<br />
| Trojan peptides are short (15aa) sequences that penetrate through the plasma membrane inside the cell without the need of any receptor or endocitosis process [Derossi 1998]. We want to use them to increase the efficiency with which the payload enters the target cell. Ideally, they should not contain Trp or His, as those are the relevant amino acids for exportation. Two good candidates are the penetratin from the HIV TAT protein, and polyarginine [Jones et al 2005].<br />
<br />
{|<br />
|<br />
{| class="wikitable"<br />
|+ Primary protein structure for penetratins<br />
! scope="row" style="background: #7ac5e8"|Penetratin <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|- <br />
|TAT<br />
|YGRKKRRQRRR<br />
|-<br />
|polyarginine <br />
|RRRRRRRRRRR<br />
|}<br />
<br />
| This proteins are not from yeast, so we needed to retro-translate them and codon-optimize them for expression in yeast. To do this we used the R package [http://www.bioconductor.org/packages/2.10/bioc/html/GeneGA.html geneGA], that takes into account codon usage and messenger secondary structure in the optimization process.<br />
|}<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Trojan-box.jpg | 200px]]<br />
{| class="wikitable"<br />
|+ Trojan peptide parts<br />
! scope="row" style="background: #7ac5e8"|Penetratin<br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|TAT<br />
|<partinfo>BBa_K792003</partinfo><br />
|-<br />
|polyarginine<br />
|<partinfo>BBa_K792004</partinfo><br />
|}<br />
<br />
|}<br />
<br />
== Payloads ==<br />
<br />
{|<br />
|-<br />
|The payloads are the elements of the synthetic gene that code for the “amino acid rich” region of the secreted protein. By “a.a. rich” we mean, rich in the amino acid we want to export, Trp or His in our case. These domains should be soluble enough not to cause precipitation of the protein, and should be relatively stable not to be degraded before they are actually secreted from the cell.<br />
<br />
<br />
We have used and contributed 4 new payload biobricks.<br />
<br />
* '''His-tag''', a polyhistidine-tag normally used for protein purification protocols, or as an epitope for commercial antibodies.<br />
* '''TrpZipper2''', a small peptide that folds into a beta-hairpin secondary structure. The indole rings of the Trp form a hydrophobic core. The protein is water soluble and monomeric [Cochran 2001]. <br />
* '''PolyHb''' and '''PolyWb''', Histidine and Tryptophan rich peptides design by us for this project.<br />
<br />
| style="width: 25%" align="center" | [[File:Bsas2012-Payload-box.jpg | 200px ]]<br />
{| class="wikitable"<br />
|+ Payloads parts<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Part<br />
|-<br />
|His tag<br />
|<partinfo>BBa_K792005</partinfo><br />
|-<br />
|TrpZipper2<br />
|<partinfo>BBa_K792006</partinfo><br />
|-<br />
|PolyHb<br />
|<partinfo>BBa_K792007</partinfo><br />
|-<br />
|PolyWb<br />
|<partinfo>BBa_K792008</partinfo><br />
|}<br />
|}<br />
{|<br />
|'''PolyHb''' and '''PolyWb''' were designed taking into acount the following consideration:<br />
# avoided repeating the same residue in tandem to minimize local tRNA depletion<br />
# avoided tryptophan in tandem because of their low solubility<br />
# we included glycine to avoid the formation of rigid structures <br />
# included acidic and basic amino acids to increase solubility<br />
|<br />
{| class="wikitable"<br />
|+ Payloads parts and protein sequences<br />
! scope="row" style="background: #7ac5e8"|Payload <br />
! scope="row" style="background: #7ac5e8"|Residue sequence<br />
|-<br />
|His tag<br />
|HNHNHNHNHNHN<br />
|-<br />
|TrpZipper2<br />
|SWTWENGKWTWK<br />
|-<br />
|PolyHb<br />
|HGDHDGHGKHKG HGDHDGHGKHKG HGDHDGHGKHKG<br />
|-<br />
|PolyWb<br />
|WGDWDGWGKWKG WGDWDGWGKWKG WGDWDGWGKWKG<br />
|}<br />
|}<br />
<br />
Retro-translating and optimizing for yeast (as explained above), we obtained the final sequences (see the registry for more details).<br />
<br />
= Implementation =<br />
<br />
'''Due to time and resources limitation''', we decided to simplify the construction (assembly) process as much as possible. The '''devices were ordered as a whole''' (as gBlocks gene fragments) instead of obtaining each constitutive biobrick part and then assembling them. Although this goes against the standard part base approach, it '''saved both time and money'''. We plan to obtain each constitutive part from the devices by PCR with suffix/prefix containing primers, as a contribution to the registry. <br />
<br />
'''We decided to use yeast expression plasmids with repressible or constitutive promoters''', to drive the expression of our devices. This decision was taken because such plasmids were readily available to us, they had adequate selection markers and they are a fairly standard approach in yeast genetics. <br />
<br />
{| style="width: 100%"<br />
| align="center" | [[File:Bsas2012-BBgeneric.jpg|850px]]<br />
|-<br />
| align="center" | '''Figure 1: DNA structure we sent to synthesis<br />
|}<br />
<br />
Although the prefix and suffix are not considered part of the device, they were included in DNA structure we the ordered for easy future assembly.<br />
Because the entire ORF is contained within the prefix and suffix, no care for in-frame assembly has to be taken. We used the original ''RFC10 BioBrick standard''.<br />
<br />
In addition, '''we included convenient restriction sites (RS) for directional cloning into the yeast expression vectors''' (''RS1'' and ''RS3''). ''RS2'' will allow easy removal of the sequence coding for the trojan peptide, by restriction and re-ligation. <br />
<br />
<br />
== Selection of restriction sites ==<br />
<br />
We yet have to choose exactly which restriction enzymes we are going to use, so we can know the final DNA sequence to include in the synthesis request.<br />
<br />
=== Considering Yeast Expression Plasmids ===<br />
<br />
{|<br />
|width = "50%" |To determine which restriction sites to use for cloning (''RS1'' and ''RS3'' in Figure 1), '''we need to know the ''MCS'' of the expression plasmids we are going to use'''. <br />
<br />
One of these plasmids will probably of the pCM180-5 series, which are centromeric plasmids with TRP1 marker, and with a doxycycline repressible promoter [Gari et al 1996]. <br />
<br />
<br />
Comparing Figure 3 and Figure 4, ''BamHI'' and ''NotI'' appear in both ''MCS'' in the same order, so they are good candidates for ''RS1'' and ''RS3'' (Figure 1) respectively.<br />
<br />
<br />
We will probably need to clone the construct in a general purpose plasmid for manipulation. For instance, to remove the trojan we need to clone the construct into a plasmid, cut it with the RE of RS2, precipitate the DNA (to get rid of the trojan fragment), and religate. For this to work we have to make sure that there is no RS2 in the vector. A common vector for this is pBluescript, wich has the MCS shown in Figure 5.<br />
<br />
|<br />
{| style="width: 100%"<br />
|+ '''Figure 3: Multiple Cloning Site (MCS) of the pCM180 series plasmids''''<br />
| align="center" | [[File:Bsas2012-bb-Fig3.png|450px]]<br />
|}<br />
<br />
<br />
----<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 4: pEG202 MCS sequence and restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig_4.png|450px]]<br />
|}<br />
<br />
|}<br />
<br />
The other plasmid we might use is ''pEG202'', with a 2 ori, HIS3 marker and a constitutive promoter (PADH1).<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 5: pBluescript Multiple Cloning Site'''<br />
| align="center" | [[File:Bsas2012-bb-Fig5.png|550px]]<br />
|}<br />
<br />
=== Considering RFC10 assembly standard restriction enzymes ===<br />
<br />
The '''restriction sites used for RS1-3 have to be different from the ones used in the BioBricks standard'''. The standard RE for BioBricks are EcoRI, NotI, XbaI, SpeI and PstI.<br />
<br />
Unfortunately, ''NotI'' was our candidate for ''RS3'', so we have a problem here. There are different solutions. We can either use two restriction sites instead of ''RS3'', one for each plasmid, or we can change the BioBrick standard to something like RFC[21] (Berkeley standard) that has no NotI restriction site. <br />
<br />
An other option would be to use the restriction sites in the prefix and suffix to clone the construct into the expression plasmid. This is appealing because we don´t need new REs. Anyhow we would need to include a RS in the 5’ end to be able to directionally clone into pCM180. The new design would look something like Figure 6.<br />
<br />
<br />
{| style="width: 100%"<br />
|+ '''Figure 6: Alternative scheme for the restriction sites'''<br />
| align="center" | [[File:Bsas2012-bb-Fig6.png|850px]]<br />
|}<br />
<br />
{|<br />
| If we make RS4 a BamHI site, we can directionally clone the construct into both plasmids (pCM180 and pEG202 ) by cutting with BamHI and NotI. In this design we would not need RS1 and RS3, but we can include them just in case we need to clone them into an other vector. <br />
<br />
Regarding RS2 we need a restriction enzyme that produces cohesive ends, codes for acceptable amino acids, is easily available and not used in an other part of the construct. Some candidate RE are listed in Table 9. <br />
| width = "40%" align="center"|<br />
{| class="wikitable"<br />
|+ '''Table 9. Candidate restriction enzymes for RS2'''<br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site sequence <br />
! scope="row" style="background: #7ac5e8"|Overhang <br />
! scope="row" style="background: #7ac5e8"|Codes for<br />
|-<br />
|HindIII <br />
|A/AGCTT <br />
|AGCT <br />
|Lys-Leu (KL)<br />
|-<br />
|XhoI <br />
|C/TCGAG <br />
|TCGA <br />
|Leu-Glu (LE)<br />
|}<br />
|}<br />
<br />
Probably any of them will work, but the trojan peptide needs to be basic so the HindIII site looks better suited. <br />
If we want to remove the trojan, we will have to clone the construct into a vector with no HindIII site. One way to do this is to clone it into pBluescript in such a way that the HindIII restriction site of the MCS is removed. <br />
<br />
Looking at Figure 5 we can see that if we cut pBluescript with XhoI and PstI, the HindIII site is removed. If we make RS1 -> XhoI (which is easily available) we can cut the construct with these same enzymes and directionally clone it into pBluescript.<br />
<br />
Most likely we wont use RS3, but we can assign it a restriction site just in case. For example NcoI could be used instead of NotI to do the directional cloning into pEG202.<br />
<br />
=== Final selection ===<br />
{|<br />
|- valign="top"<br />
| width = "55%" | <br />
<br />
Taking in count everything we mentioned above, this is the final selection restriction enzymes we are going to use. Refer to ''Figure 1'' to check localization of each restriction site.<br />
| align = "center" |<br />
{| class="wikitable" style="width:75%"<br />
! scope="row" style="background: #7ac5e8"|RS# <br />
! scope="row" style="background: #7ac5e8"|R. Enzyme <br />
! scope="row" style="background: #7ac5e8"|R. Site Sequence<br />
|-<br />
|RS1 <br />
|XhoI <br />
|C/TCGAG<br />
|-<br />
|RS2 <br />
|HindIII <br />
|A/AGCTT<br />
|-<br />
|RS3 <br />
|NcoI <br />
|C/CATGG<br />
|-<br />
|RS4 <br />
|BamHI <br />
|G/GATCC<br />
|}<br />
|}<br />
<br />
=== Additional notes ===<br />
<br />
When we order the construct we will probably have to specify in which vector we want it shipped. RS3 (and perhaps RS1) could be changed depending on this vector. For example we could order the gene in pBluescript, in which case it might be convenient to make RS3 -> SacI. For other plasmids other RS might be needed, but if possible it would be convenient to retain RS1 -> XhoI that allows us to clone the gene into pBluescript as described above.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:29:56Z<p>Abush84: /* Steady State Solution */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/ModelAdvance#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model, doing dedicated experiments or using values from the literature. This allowed to check the feasibility of the system.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture density after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
*K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
*K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here, in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the AA rich exportation peptide, the export rate would equal P_MAX. Of course this is not possible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
From [http://www.biomedcentral.com/1752-0509/2/87| von der Haar 2008] we get an estimate for the total number of elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ ]<br />
<br />
* P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated in the simulation by &epsilon; and '''p''', to alter the fraction between populations.<br />
<br />
*p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
*p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the P_MAX value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell ) * relative abundance of AA * Navog / AA's molar mass.<br />
<br />
*d trp= 2.630e7 AA / cell <br />
<br />
*d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:27:31Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model, doing dedicated experiments or using values from the literature. This allowed to check the feasibility of the system.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture desnity after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
*K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
*K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the AA rich exportation peptide, the export rate would equal P_MAX. Of course this is not possible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
From [http://www.biomedcentral.com/1752-0509/2/87| von der Haar 2008] we get an estimate for the total number of elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ ]<br />
<br />
* P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated in the simulation by &epsilon; and '''p''', to alter the fraction between populations.<br />
<br />
*p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
*p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the P_MAX value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell ) * relative abundance of AA * Navog / AA's molar mass.<br />
<br />
*d trp= 2.630e7 AA / cell <br />
<br />
*d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:06:29Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the exportation peptide, the export rate would equal P_MAX. Of course this is imposible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ [http://www.biomedcentral.com/1752-0509/2/87| von der Haar 2008] ]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated in the simulation (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
*p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
*p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell ) * relative abundance of AA * Navog / AA's molar mass.<br />
<br />
<br />
*d trp= 2.630e7 AA / cell <br />
<br />
*d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:05:10Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the exportation peptide, the export rate would equal P_MAX. Of course this is imposible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ [http://www.biomedcentral.com/1752-0509/2/87| von der Haar 2008] ]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated in the simulation (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
*p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
*p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * Navog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:03:04Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the exportation peptide, the export rate would equal P_MAX. Of course this is imposible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [http://www.biomedcentral.com/1752-0509/2/87| von der Haar 2008]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T02:00:03Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|200px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 2.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 2. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of protein elongation events (peptidil transferase reactions) for a yeast cell per hour. That is, if all the biosynthetic capacity of the cell was used to create the exportation peptide, the export rate would equal P_MAX. Of course this is imposible because the cell has to do many other things, therefore we considered 1% of P_MAX as a reasonable upper bound for '''p'''.<br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T01:54:46Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
=== Steady State Solution ===<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
<br />
=== Parameter selection ===<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|300px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 11.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 11. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The best fit to the data is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given a top value (P_MAX) 1% of the maximum estimated number of elongation events for a yeast cell per hour. Our model does not explicitly include time as a variable, that means that the production is switched ON all the time. <br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
==Numerical Simulations==<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
<br />
== Die or thrive?==<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
== Parameter Space and Solutions ==<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
===Conclusions===<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T01:04:11Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1-(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
===== Steady State Solution =====<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (in green, purple and red).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
== Model Analysis ==<br />
:The standard procedure when studying the temporal evolution of a non-linear system is to try to determine all the possible types of trajectories in the phase space. First the nullclines and fixed points (FP) are found; then the latter are classified according to their stability; in the same way as a linear system [Strogatz]. The goal is to qualitatively draw the trajectories for any initial conditions to see how the variables evolve.<br />
<br />
Some relevant questions we can answer with this approach are:<br />
<br />
* How is this behavior governed by the model's parameters?<br />
<br />
* How do the trajectories change as the parameters vary? Does any new FP or close orbit appear?<br />
<br />
:There was a major difficulty in the analysis of the model because of the dimension of the problem - 4 variables. The analytical results yielded by the usual tools (nullclines, system linearization) could not be simplified so that all the relevant properties of the system could be understood in terms of the parameters. <br />
<br />
:We obtained the 4x4 Jacobian - evaluated in the FP - and later its eigenvalues. The expression found for them, a function of the relevant parameters, is too long and can't be simplified in a way that reveals changes in stability. <br />
<br />
:Besides that, we couldn't do a visual analysis of the nullclines to have a qualitative idea of what goes on.<br />
<br />
#A very common way to proceed in such a case is to reduce the model, ideally to a two-dimensional system. Sometimes one of the variables varies much more slowly/faster than the ones we are interested in, that it can be considered constant when studying the dynamics of the variables we are interested in. This is a standard procedure in the study of chemical reactions called Quasi Steady State Approximation. <br />
# We also noticed that the Hill coefficients are similar to one. So we explored the possibility of approximating them to one; is there a relevant change or not?<br />
<br />
:(**)Phase Plane review.<br />
<br />
===Numerical Simulations===<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
=== Model Reduction: Quasy Steady State Approximation ===<br />
<br />
:To gain some insight into the behavior of the model, we assumed that the variation of AA concentration in the medium is much faster than growth of the yeast populations. <br />
<br />
[[File:bsas2012-modeling-eq15.png]](6)<br />
<br />
: Therefore taking dAAj/dt=0, we re-wrote the Amino Acids as a function of ('''Na''' and '''Nb''') in a reduced 2x2 model for the evolution of the populations. <br />
<br />
[[File:bsas2012-modeling-eq16.png]](model_aprox)<br />
<br />
:Thus a phase portrait for the nullclines and trajectories was drawn. <br />
<br />
[[File:bsas2012-modeling-fignull.png]]<br />
<br />
Figure 2. Phase portrait for the reduce model.<br />
<br />
::Although the reduced model presents the same fixed point, it’s a saddle point. <br />
<br />
:What does that mean? Pick any point for the plane; those are your initial conditions for the concentration of each population. Now to see how the community evolves with time follow the arrows - like a particle in a flow. No matter how close to the FP we start, the community is overtaken by one of the two strains. <br />
<br />
* Only by taking i.c. along the stable manifold –direction marked between red arrows for clarification in Figure 2 – do we reach the FP we wanted as t &rarr; &infin;.<br />
* The stable FP are {Na &rarr; Nt, Nb &rarr; 0} and {Na &rarr; 0, Nb &rarr; Nt}. <br />
<br />
:This phase portrait is the one we'd expect for two ''species'' competing for the same resources. The ''Principle of Competitive Exclusion'' states that they can't typically coexist. <br />
<br />
::This is not a representation of auxotrophic community! Somehow we've landed on a Lotka-Volterra example.<br />
<br />
:The linearization of the set of ODEs around the FP yields instability regardless of the parameters. One of the eigenvalues is always positive. <br />
<br />
::{2death; death (1-√((pb pa)/(db da))/death)} <br />
<br />
:There is no agreement between the predictions found here and our simulations, that usually reach a non-trivial SS. We won’t pursue this reduction any further.<br />
<br />
=== Die or thrive?===<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
===Parameter Space and Solutions ===<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
====Conclusions====<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.<br />
<br />
====Effect of the Hill coeffient====<br />
<br />
:''How are the '''Kaa''' relevant when they haven't appear so far? ''<br />
<br />
::Originally we took both Hill coefficients equal to one in our numerical simulations, is close enough, right?<br />
<br />
:Well, the whole system didn't seem viable; it took over 100 hours for the fastest cultures to grow. Now using the fitted coefficients; the time scale becomes reasonable again.<br />
<br />
<br />
[[File:bsas2012-modeling-fig5.png]]<br />
<br />
Figure 5. Evolution in time of two cultures that only differ in the i.c.c. with both Hill coef = 1(left); and both Hill coef &gt; 1(right). <br />
<br />
* When n_ hill =1 only the culture with higher i.c.c. reaches a non-trivial SS. The other slowly dies. <br />
* There is no clear difference between the cases with high and low i.c.c. for n_ hill =1.5. Note that they reach SS faster as well.<br />
<br />
:The survival of an auxotrophic community is dependent on both the apparent dissociation constants (Ks) and the Hill coefficient as shown clearly on Figure 5. There are two effects related to Hill parameters ''K and n_hill'' here:<br />
<br />
Shorter time scale to SS.<br />
<br />
Larger subspace of i.c.c. that lead to SS.<br />
<br />
:So when creating an auxotrophy, pick a nutrient with an appropriate Hill coefficient and K. Check the ''range'' of allowed i.c.c. estimating the values of the rest of the parameters. We could say that the model is more stable as more sets of initial conditions are susceptible of regulation. <br />
<br />
:Our case is similar to the one shown in figure 5 with both hill coefficients &rarr; 1. Region III is less sensitive to variations in the i.c.c. and therefore the actual Region III resembles the theorical one in Figure 4. When n_hill=1 is much smaller.<br />
<br />
=== Relaxation time===<br />
<br />
:Not only are we interested in predicting the fraction of each strain in the culture, we need this control within a time frame or the system is irrelevant.<br />
<br />
:Let’s call &Tau; the time it takes one of the populations to reach its SS value (relaxation time). We rely again on numerical simulations to obtain those set of parameters where &Tau; is less than 65 hours, for example.<br />
<br />
:Creating a mesh we explored the parameter space (p, &epsilon;) for different initial conditions i.c.c. to visualize when the model’s ''prediction'' and the ''numerical result'' of the simulations for the '''mole fraction''' in SS differ in an amount lower than the given error; before 65 hours. We won’t limit the mesh to Region III only.<br />
<br />
::We’ve taken 5% of the value as error; considering it an accurate estimation of an experimental measure’s precision. <br />
<br />
::For each set of conditions ('''p''', &epsilon;,i.c.c.) we test the agreement between the proposed regulation and the NS.<br />
<br />
:The first two graphs in Figure 6 are different orientations in space of a '''binary test''', a '''blue dot''' indicates the points in this space where the error is smaller than 5%. Connecting the dots we see a pattern. This area in the parameter space where we can control the community through regulation has the same shape as Region III, although they don't overlap perfectly. <br />
<br />
:The '''green bubble's size''' in the third graph is a measure of the '''error''' in each point for the same NS; it ranges from 0.6 to 4%. The points with larger error are the ones in the area between II and III.<br />
<br />
:Some points well within Region III don't happen to comply with the established time limit. However there are enough points to see that all percentages are accessible with the model for the chosen precision. It's not necessary now, but a closer look at specific sections is possible with a higher point density matrix. <br />
<br />
[[File:bsas2012-modeling-fig6b.png]]<br />
<br />
[[File:bsas2012-modeling-fig6bi.png]]<br />
<br />
Figure 6. Scatter plot of the conditions under which the modeled auxotrophy leads to community control in under 65 hours with a 5% precision.<br />
<br />
=== Oscillations===<br />
<br />
:Damp oscillations are observed!!<br />
<br />
:This interesting effect was found while doing random searchs through our paramater space. It appears if:<br />
<br />
* the initial concentration of AA in the medium are of the order of '''Kaa''' and '''Kbb''' and<br />
* the parameters that regulate the production and export of AA are from region II in Figure 4 and <br />
* we work with the approximate model where both n_hill= 1. <br />
<br />
[[File:bsas2012-modeling-fig7.png]]<br />
<br />
Figure 7. Evolution of the two populations in the community vs time when the conditions above are met. Both populations fluctuate with time in such a way that Nt remains constant. <br />
<br />
:We were unable to numerically find parameters where the oscillation period was shorter.<br />
<br />
== Model Transformation: a new analysis ==<br />
<br />
Let’s review what we’ve learned so far from our system.<br />
<br />
* Seems stable, <br />
<br />
* capable of oscillations, <br />
<br />
* there are two cases with a stable population and a defined mole fraction for each strain, one where the AAs are in SS and one where they aren’t. <br />
<br />
* The set of i.c. for which the culture thrives is dependent on the Ks. <br />
<br />
Yet all this information is not so trivial to find – especially the oscillations discovery was a fluke. <br />
<br />
There had to be a transformation where all these properties were more accessible or evident.<br />
<br />
What we did so far was to write the results in more convenient variables. <br />
<br />
Now we will try to find a transformation to a new set of variables, whose evolution is simpler to study and write a new set of ODEs for them.<br />
<br />
For instance the AAs only appear as the argument of Hill functions, so why not just work with those rations. We defined:<br />
<br />
[[File:bsas2012-modeling-eqt1.jpg]] (12)<br />
<br />
This is better because now &xi; is constant when:<br />
<br />
# AAa reachs steady state.<br />
<br />
# Na exports more that could Nb possibly adsorb and AAa accumulates in the medium. For AAi >> Ki the ratio &xi; &rarr; k_max.<br />
<br />
The same argument is valid for &mu;.<br />
<br />
Then we take the obvious choices to describe the percentage of each and the total population of the community. <br />
<br />
[[File:bsas2012-modeling-eqt2.jpg]] (13)<br />
<br />
Now we must find differential equations that describe their dynamic.<br />
<br />
For example:<br />
[[File:bsas2012-modeling-eqt3.jpg]] (14)<br />
<br />
and <br />
<br />
[[File:bsas2012-modeling-eqt4.jpg]] (15)<br />
<br />
The new set of ODEs with m,j >1 is:<br />
<br />
[[File:bsas2012-modeling-eqt6.jpg]] (transf_model)<br />
<br />
The model with m=j=1 is in the Appendix.<br />
<br />
* The AAs can be zero, so can the new variables. Rewriting the equation we see that each ratio is elevated to 2- (s+1)/s &gt; 0 for s &gt; 1; so the variables are not really in the denominator.<br />
<br />
* &chi; stop growing (is constant) if and only if &mu; = &xi;, n can’t equal 1 since n=1 doesn’t satisfy the DE for n.<br />
<br />
* This system has 4 solutions. The only physical one, is the one we already know: equation (3) in the old variables.<br />
<br />
* If &xi; = k_max, the culture again reaches a SS, but the fraction &chi; is indeterminate.<br />
<br />
Here’s the great improvement over the old variables. The SS for &chi;, &xi; and &mu; is independent of n; i.e. the conditions for which their DE equal zero. This means that the nullclines are independent as well. Now we have a reduction in the system's dimension. Same SS as 4x4 but this 3x3 system can be analyzed visually,<br />
<br />
* We can represent the vector field [[File:Bsas2012-modeling-eqt9.png]] in space and track a trajectory that starts at (&chi;0, &xi;0, &mu;0). <br />
* We can plot the nullclines, see where they meet; do a quick stability test by checking the sign of the vector field around the FP. <br />
<br />
[[File:bsas2012-modeling-fig9.jpg]]<br />
<br />
Figure 8. Nullclines for the reduced model in the new variables &chi;, &xi; and &mu;.<br />
<br />
=== Stability of the regulation. === <br />
<br />
Let’s directly go to the linearization of our 3 ODEs close to the FP where there is regulation through the auxotrophy.<br />
<br />
We said that n wasn’t relevant to the fixed point's location, but now we have to consider it while examining the eigenvalues. <br />
<br />
Once again the expresion for the three eigenvalues &gamma; is very long. We replace the mayority of the parameters with the value we estimated, and n for its SS value - though this is not always correct. <br />
<br />
We are able now to study the stability of the FP when we vary p_a and p_b. <br />
<br />
Reminder: The FP is not a stable node if at least one of the eigenvalues is positive.<br />
<br />
[[File:bsas2012-modeling-fig10b.jpg]]<br />
Figure 9. Visual aid to determine how the eigenvalue's sign changes with model's parameters '''p''' and &epsilon;.<br />
<br />
Figure 9 shows a top view of the eigenvalues vs ('''p''',&epsilon;). All the information we need can be ''read'' from them. The first and third &gamma; present a clear change in behavior that we accentuated with the green line, we present also a side view for the second &gamma; since it's not so obvious. The eigenvalues change sign there; positive below and negative above the line. <br />
<br />
*The region where the eigenvalues are positive, corresponds to a unstable FP. Comparing Figure 9 with Figure 4 reveals that we are in fact in what we called Region I. The trajectories tend to another FP: Nt &rarr; 0.<br />
<br />
*The three eigenvalues are negative above this line we drew: the FP it's a stable node in this region of the parameter space. This corresponds with Region III. <br />
<br />
*There's another area up in the right corner where the values are not plotted. Taking ('''p''', &epsilon;) from that region we obtain complex eigenvalues; this is well within Region II. It's very likely that some values from II do have real negative eigenvalues plotted here.<br />
<br />
Next are some examples of complex '''&gamma;''':<br />
(1,1) &rarr; {-35537.4 + 85795. i, -9334.41 + 5389.23 i, -0.0119048 - 3.99219*10^-9 i}<br />
(0.74,0.74 ) &rarr; {-2142.41 + 5172.24 i, -513.323 + 296.37 i, -0.0119049 - 7.1472*10^-8 i}<br />
<br />
In these cases the solutions for (n (t), &chi;(t), &xi;(t), &mu;(t))are not real numbers. These coincide with the values that made MATLAB scream during the NS: there is no real solution.<br />
<br />
==== Oscillations Revised====<br />
<br />
The oscillations in Figure 7 correspond to the model where the Hill coefficients are set to 1. Though we tried, we couldn't ''manually'' find a set of parameters and i.c. so that they appear when using the model with the correct Hill coefficients.<br />
<br />
Is this always the case? Could we find the answer using the new variables?<br />
<br />
We found a tendency by sampling random values ('''p''',&epsilon;) from the region where the eigenvalues are complex and i.c. &chi;0, &xi;0, &mu;0. So far the examples from the section above are representative of our findings:<br />
<br />
It seems that when the eigenvalues of the linearization around the FP are complex so is the result for the time evolution of the variables when n_hill &gt; 1. <br />
<br />
Still, this doesn't exclude the possibility of bifurcations that change the stability or simply later finding a small subset where oscillations occur. Just that we were unable to completely characerize the behavior due to the complexity of the problem.<br />
<br />
<br />
The next figure shows some of the i.c. for which damp oscillations exist in the first case. <br />
<br />
[[File:bsas2012-modeling-fig11.jpg]]<br />
<br />
Figure 10. Oscillations in the fraction of each population when the AAs are initially saturated.<br />
<br />
Figure 10 presents the oscillations obtained for &chi;0 = 0.3, 3, 9 in black,red and blue. We varied the initial total population through n0= 1:2,1:20,1:200 for each &chi;0 and found little variance in such case.<br />
<br />
The eigenvalues obtain have for the parameters in Figure 7 are: <br />
(0.5, 0.5) &rarr; {-0.0734764, -0.0206177 - 0.0211589 i, -0.0206177 + 0.0211589 i}<br />
<br />
The second and third are complex conjugates with real part &lt; 0. Together they generate what is called a stable spiral in the eigenvector space: all ''close'' trajectories are attracted to it; the flow is the like water whirling in a sink.<br />
This means that when we plot &chi; vs time we see that the oscillations around a value grow smaller. &chi; tends to that value as t &rarr; &infin;.<br />
<br />
The more distintive characteristics of the oscillations are<br />
<br />
# AA must be initally saturated.<br />
# The period is huge: over a thousand hours; well above the 50 hour given by the linearization. <br />
# The period is not constant, this is due to the non-linear nature of the system.<br />
# There is a dependance with the initial fraction &chi;0 but not on the intial dilution n0.<br />
<br />
== Parameter selection ==<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|300px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 11.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 11. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The data's best fit is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
*K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
*K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within and among strains were considered equal. The system's general ''carrying capacity'' considered in the model was defined as the OD of a saturated culture, that corresponds to 3e7 #CFU/ml (Colony forming units):<br />
<br />
*Cc= 3e7 #cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given an upper bound value (P_MAX) of 1% of the maximum estimated number of elongation events for a yeast cell per hour. Our model does not explicitly include time as a variable, that means that the production is switched ON all the time. <br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
== Appendix ==<br />
<br />
=== Model : SS solution ===<br />
<br />
1. Nt -> 0<br />
<br />
2. [[File:Bsas2012-modeling-eqsol1.png]]<br />
<br />
3. [[File:Bsas2012-modeling-eqsol3.png]]<br />
<br />
=== Transformed Model: SS===<br />
'''Steady states''' <br />
<br />
[[File:Bsas2012-modeling-soltrans.png]]<br />
<br />
The '''model''' obtained when m=j=1 differs:<br />
<br />
[[File:Bsas2012-modeling-modtrans1.png]]<br />
<br />
However the solutions remain the same.<br />
<br />
= References =</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-27T00:54:50Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1+(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
===== Steady State Solution =====<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (colors).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
== Model Analysis ==<br />
:The standard procedure when studying the temporal evolution of a non-linear system is to try to determine all the possible types of trajectories in the phase space. First the nullclines and fixed points (FP) are found; then the latter are classified according to their stability; in the same way as a linear system [Strogatz]. The goal is to qualitatively draw the trajectories for any initial conditions to see how the variables evolve.<br />
<br />
Some relevant questions we can answer with this approach are:<br />
<br />
* How is this behavior governed by the model's parameters?<br />
<br />
* How do the trajectories change as the parameters vary? Does any new FP or close orbit appear?<br />
<br />
:There was a major difficulty in the analysis of the model because of the dimension of the problem - 4 variables. The analytical results yielded by the usual tools (nullclines, system linearization) could not be simplified so that all the relevant properties of the system could be understood in terms of the parameters. <br />
<br />
:We obtained the 4x4 Jacobian - evaluated in the FP - and later its eigenvalues. The expression found for them, a function of the relevant parameters, is too long and can't be simplified in a way that reveals changes in stability. <br />
<br />
:Besides that, we couldn't do a visual analysis of the nullclines to have a qualitative idea of what goes on.<br />
<br />
#A very common way to proceed in such a case is to reduce the model, ideally to a two-dimensional system. Sometimes one of the variables varies much more slowly/faster than the ones we are interested in, that it can be considered constant when studying the dynamics of the variables we are interested in. This is a standard procedure in the study of chemical reactions called Quasi Steady State Approximation. <br />
# We also noticed that the Hill coefficients are similar to one. So we explored the possibility of approximating them to one; is there a relevant change or not?<br />
<br />
:(**)Phase Plane review.<br />
<br />
===Numerical Simulations===<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
=== Model Reduction: Quasy Steady State Approximation ===<br />
<br />
:To gain some insight into the behavior of the model, we assumed that the variation of AA concentration in the medium is much faster than growth of the yeast populations. <br />
<br />
[[File:bsas2012-modeling-eq15.png]](6)<br />
<br />
: Therefore taking dAAj/dt=0, we re-wrote the Amino Acids as a function of ('''Na''' and '''Nb''') in a reduced 2x2 model for the evolution of the populations. <br />
<br />
[[File:bsas2012-modeling-eq16.png]](model_aprox)<br />
<br />
:Thus a phase portrait for the nullclines and trajectories was drawn. <br />
<br />
[[File:bsas2012-modeling-fignull.png]]<br />
<br />
Figure 2. Phase portrait for the reduce model.<br />
<br />
::Although the reduced model presents the same fixed point, it’s a saddle point. <br />
<br />
:What does that mean? Pick any point for the plane; those are your initial conditions for the concentration of each population. Now to see how the community evolves with time follow the arrows - like a particle in a flow. No matter how close to the FP we start, the community is overtaken by one of the two strains. <br />
<br />
* Only by taking i.c. along the stable manifold –direction marked between red arrows for clarification in Figure 2 – do we reach the FP we wanted as t &rarr; &infin;.<br />
* The stable FP are {Na &rarr; Nt, Nb &rarr; 0} and {Na &rarr; 0, Nb &rarr; Nt}. <br />
<br />
:This phase portrait is the one we'd expect for two ''species'' competing for the same resources. The ''Principle of Competitive Exclusion'' states that they can't typically coexist. <br />
<br />
::This is not a representation of auxotrophic community! Somehow we've landed on a Lotka-Volterra example.<br />
<br />
:The linearization of the set of ODEs around the FP yields instability regardless of the parameters. One of the eigenvalues is always positive. <br />
<br />
::{2death; death (1-√((pb pa)/(db da))/death)} <br />
<br />
:There is no agreement between the predictions found here and our simulations, that usually reach a non-trivial SS. We won’t pursue this reduction any further.<br />
<br />
=== Die or thrive?===<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
===Parameter Space and Solutions ===<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
====Conclusions====<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.<br />
<br />
====Effect of the Hill coeffient====<br />
<br />
:''How are the '''Kaa''' relevant when they haven't appear so far? ''<br />
<br />
::Originally we took both Hill coefficients equal to one in our numerical simulations, is close enough, right?<br />
<br />
:Well, the whole system didn't seem viable; it took over 100 hours for the fastest cultures to grow. Now using the fitted coefficients; the time scale becomes reasonable again.<br />
<br />
<br />
[[File:bsas2012-modeling-fig5.png]]<br />
<br />
Figure 5. Evolution in time of two cultures that only differ in the i.c.c. with both Hill coef = 1(left); and both Hill coef &gt; 1(right). <br />
<br />
* When n_ hill =1 only the culture with higher i.c.c. reaches a non-trivial SS. The other slowly dies. <br />
* There is no clear difference between the cases with high and low i.c.c. for n_ hill =1.5. Note that they reach SS faster as well.<br />
<br />
:The survival of an auxotrophic community is dependent on both the apparent dissociation constants (Ks) and the Hill coefficient as shown clearly on Figure 5. There are two effects related to Hill parameters ''K and n_hill'' here:<br />
<br />
Shorter time scale to SS.<br />
<br />
Larger subspace of i.c.c. that lead to SS.<br />
<br />
:So when creating an auxotrophy, pick a nutrient with an appropriate Hill coefficient and K. Check the ''range'' of allowed i.c.c. estimating the values of the rest of the parameters. We could say that the model is more stable as more sets of initial conditions are susceptible of regulation. <br />
<br />
:Our case is similar to the one shown in figure 5 with both hill coefficients &rarr; 1. Region III is less sensitive to variations in the i.c.c. and therefore the actual Region III resembles the theorical one in Figure 4. When n_hill=1 is much smaller.<br />
<br />
=== Relaxation time===<br />
<br />
:Not only are we interested in predicting the fraction of each strain in the culture, we need this control within a time frame or the system is irrelevant.<br />
<br />
:Let’s call &Tau; the time it takes one of the populations to reach its SS value (relaxation time). We rely again on numerical simulations to obtain those set of parameters where &Tau; is less than 65 hours, for example.<br />
<br />
:Creating a mesh we explored the parameter space (p, &epsilon;) for different initial conditions i.c.c. to visualize when the model’s ''prediction'' and the ''numerical result'' of the simulations for the '''mole fraction''' in SS differ in an amount lower than the given error; before 65 hours. We won’t limit the mesh to Region III only.<br />
<br />
::We’ve taken 5% of the value as error; considering it an accurate estimation of an experimental measure’s precision. <br />
<br />
::For each set of conditions ('''p''', &epsilon;,i.c.c.) we test the agreement between the proposed regulation and the NS.<br />
<br />
:The first two graphs in Figure 6 are different orientations in space of a '''binary test''', a '''blue dot''' indicates the points in this space where the error is smaller than 5%. Connecting the dots we see a pattern. This area in the parameter space where we can control the community through regulation has the same shape as Region III, although they don't overlap perfectly. <br />
<br />
:The '''green bubble's size''' in the third graph is a measure of the '''error''' in each point for the same NS; it ranges from 0.6 to 4%. The points with larger error are the ones in the area between II and III.<br />
<br />
:Some points well within Region III don't happen to comply with the established time limit. However there are enough points to see that all percentages are accessible with the model for the chosen precision. It's not necessary now, but a closer look at specific sections is possible with a higher point density matrix. <br />
<br />
[[File:bsas2012-modeling-fig6b.png]]<br />
<br />
[[File:bsas2012-modeling-fig6bi.png]]<br />
<br />
Figure 6. Scatter plot of the conditions under which the modeled auxotrophy leads to community control in under 65 hours with a 5% precision.<br />
<br />
=== Oscillations===<br />
<br />
:Damp oscillations are observed!!<br />
<br />
:This interesting effect was found while doing random searchs through our paramater space. It appears if:<br />
<br />
* the initial concentration of AA in the medium are of the order of the Ks and<br />
* the parameters that regulate the production and export of AA are from region II in Figure 4 and <br />
* we work with the approximate model where both n_hill= 1. <br />
<br />
[[File:bsas2012-modeling-fig7.png]]<br />
<br />
Figure 7. Evolution of the two populations in the community vs time when the conditions above are met. Both populations fluctuate with time in such a way that Nt remains constant. <br />
<br />
:We were unable to numerically find parameters where the oscillation period was shorter.<br />
<br />
== Model Transformation: a new analysis ==<br />
<br />
Let’s review what we’ve learned so far from our system.<br />
<br />
* Seems stable, <br />
<br />
* capable of oscillations, <br />
<br />
* there are two cases with a stable population and a defined mole fraction for each strain, one where the AAs are in SS and one where they aren’t. <br />
<br />
* The set of i.c. for which the culture thrives is dependent on the Ks. <br />
<br />
Yet all this information is not so trivial to find – especially the oscillations discovery was a fluke. <br />
<br />
There had to be a transformation where all these properties were more accessible or evident.<br />
<br />
What we did so far was to write the results in more convenient variables. <br />
<br />
Now we will try to find a transformation to a new set of variables, whose evolution is simpler to study and write a new set of ODEs for them.<br />
<br />
For instance the AAs only appear as the argument of Hill functions, so why not just work with those rations. We defined:<br />
<br />
[[File:bsas2012-modeling-eqt1.jpg]] (12)<br />
<br />
This is better because now &xi; is constant when:<br />
<br />
# AAa reachs steady state.<br />
<br />
# Na exports more that could Nb possibly adsorb and AAa accumulates in the medium. For AAi >> Ki the ratio &xi; &rarr; k_max.<br />
<br />
The same argument is valid for &mu;.<br />
<br />
Then we take the obvious choices to describe the percentage of each and the total population of the community. <br />
<br />
[[File:bsas2012-modeling-eqt2.jpg]] (13)<br />
<br />
Now we must find differential equations that describe their dynamic.<br />
<br />
For example:<br />
[[File:bsas2012-modeling-eqt3.jpg]] (14)<br />
<br />
and <br />
<br />
[[File:bsas2012-modeling-eqt4.jpg]] (15)<br />
<br />
The new set of ODEs with m,j >1 is:<br />
<br />
[[File:bsas2012-modeling-eqt6.jpg]] (transf_model)<br />
<br />
The model with m=j=1 is in the Appendix.<br />
<br />
* The AAs can be zero, so can the new variables. Rewriting the equation we see that each ratio is elevated to 2- (s+1)/s &gt; 0 for s &gt; 1; so the variables are not really in the denominator.<br />
<br />
* &chi; stop growing (is constant) if and only if &mu; = &xi;, n can’t equal 1 since n=1 doesn’t satisfy the DE for n.<br />
<br />
* This system has 4 solutions. The only physical one, is the one we already know: equation (3) in the old variables.<br />
<br />
* If &xi; = k_max, the culture again reaches a SS, but the fraction &chi; is indeterminate.<br />
<br />
Here’s the great improvement over the old variables. The SS for &chi;, &xi; and &mu; is independent of n; i.e. the conditions for which their DE equal zero. This means that the nullclines are independent as well. Now we have a reduction in the system's dimension. Same SS as 4x4 but this 3x3 system can be analyzed visually,<br />
<br />
* We can represent the vector field [[File:Bsas2012-modeling-eqt9.png]] in space and track a trajectory that starts at (&chi;0, &xi;0, &mu;0). <br />
* We can plot the nullclines, see where they meet; do a quick stability test by checking the sign of the vector field around the FP. <br />
<br />
[[File:bsas2012-modeling-fig9.jpg]]<br />
<br />
Figure 8. Nullclines for the reduced model in the new variables &chi;, &xi; and &mu;.<br />
<br />
=== Stability of the regulation. === <br />
<br />
Let’s directly go to the linearization of our 3 ODEs close to the FP where there is regulation through the auxotrophy.<br />
<br />
We said that n wasn’t relevant to the fixed point's location, but now we have to consider it while examining the eigenvalues. <br />
<br />
Once again the expresion for the three eigenvalues &gamma; is very long. We replace the mayority of the parameters with the value we estimated, and n for its SS value - though this is not always correct. <br />
<br />
We are able now to study the stability of the FP when we vary p_a and p_b. <br />
<br />
Reminder: The FP is not a stable node if at least one of the eigenvalues is positive.<br />
<br />
[[File:bsas2012-modeling-fig10b.jpg]]<br />
Figure 9. Visual aid to determine how the eigenvalue's sign changes with model's parameters '''p''' and &epsilon;.<br />
<br />
Figure 9 shows a top view of the eigenvalues vs ('''p''',&epsilon;). All the information we need can be ''read'' from them. The first and third &gamma; present a clear change in behavior that we accentuated with the green line, we present also a side view for the second &gamma; since it's not so obvious. The eigenvalues change sign there; positive below and negative above the line. <br />
<br />
*The region where the eigenvalues are positive, corresponds to a unstable FP. Comparing Figure 9 with Figure 4 reveals that we are in fact in what we called Region I. The trajectories tend to another FP: Nt &rarr; 0.<br />
<br />
*The three eigenvalues are negative above this line we drew: the FP it's a stable node in this region of the parameter space. This corresponds with Region III. <br />
<br />
*There's another area up in the right corner where the values are not plotted. Taking ('''p''', &epsilon;) from that region we obtain complex eigenvalues; this is well within Region II. It's very likely that some values from II do have real negative eigenvalues plotted here.<br />
<br />
Next are some examples of complex '''&gamma;''':<br />
(1,1) &rarr; {-35537.4 + 85795. i, -9334.41 + 5389.23 i, -0.0119048 - 3.99219*10^-9 i}<br />
(0.74,0.74 ) &rarr; {-2142.41 + 5172.24 i, -513.323 + 296.37 i, -0.0119049 - 7.1472*10^-8 i}<br />
<br />
In these cases the solutions for (n (t), &chi;(t), &xi;(t), &mu;(t))are not real numbers. These coincide with the values that made MATLAB scream during the NS: there is no real solution.<br />
<br />
==== Oscillations Revised====<br />
<br />
The oscillations in Figure 7 correspond to the model where the Hill coefficients are set to 1. Though we tried, we couldn't ''manually'' find a set of parameters and i.c. so that they appear when using the model with the correct Hill coefficients.<br />
<br />
Is this always the case? Could we find the answer using the new variables?<br />
<br />
We found a tendency by sampling random values ('''p''',&epsilon;) from the region where the eigenvalues are complex and i.c. &chi;0, &xi;0, &mu;0. So far the examples from the section above are representative of our findings:<br />
<br />
It seems that when the eigenvalues of the linearization around the FP are complex so is the result for the time evolution of the variables when n_hill &gt; 1. <br />
<br />
Still, this doesn't exclude the possibility of bifurcations that change the stability or simply later finding a small subset where oscillations occur. Just that we were unable to completely characerize the behavior due to the complexity of the problem.<br />
<br />
<br />
The next figure shows some of the i.c. for which damp oscillations exist in the first case. <br />
<br />
[[File:bsas2012-modeling-fig11.jpg]]<br />
<br />
Figure 10. Oscillations in the fraction of each population when the AAs are initially saturated.<br />
<br />
Figure 10 presents the oscillations obtained for &chi;0 = 0.3, 3, 9 in black,red and blue. We varied the initial total population through n0= 1:2,1:20,1:200 for each &chi;0 and found little variance in such case.<br />
<br />
The eigenvalues obtain have for the parameters in Figure 7 are: <br />
(0.5, 0.5) &rarr; {-0.0734764, -0.0206177 - 0.0211589 i, -0.0206177 + 0.0211589 i}<br />
<br />
The second and third are complex conjugates with real part &lt; 0. Together they generate what is called a stable spiral in the eigenvector space: all ''close'' trajectories are attracted to it; the flow is the like water whirling in a sink.<br />
This means that when we plot &chi; vs time we see that the oscillations around a value grow smaller. &chi; tends to that value as t &rarr; &infin;.<br />
<br />
The more distintive characteristics of the oscillations are<br />
<br />
# AA must be initally saturated.<br />
# The period is huge: over a thousand hours; well above the 50 hour given by the linearization. <br />
# The period is not constant, this is due to the non-linear nature of the system.<br />
# There is a dependance with the initial fraction &chi;0 but not on the intial dilution n0.<br />
<br />
== Parameter selection ==<br />
<br />
We estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The '''Kaa''' found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves of OD600 vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|300px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 11.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 11. Single strain culture's OD600 after over night growth vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The data's best fit is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] * Navog / (Molar mass AA) <br />
<br />
where [AA 1x] = 0.02 mg/ml is the concentration of the AA (His or Trp) in the 1x medium and Navog is Avogadro's number. We get<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml = 1.04e16 molecules/ml <br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml = 2.56e16 molecules /ml<br />
<br />
<br />
*The competition within a strain and with the other were taken as equal. The system's general ''carrying capacity'' considered in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 #cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given a top value (P_MAX) 1% of the maximum estimated number of elongation events for a yeast cell per hour. Our model does not explicitly include time as a variable, that means that the production is switched ON all the time. <br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
== Appendix ==<br />
<br />
=== Model : SS solution ===<br />
<br />
1. Nt -> 0<br />
<br />
2. [[File:Bsas2012-modeling-eqsol1.png]]<br />
<br />
3. [[File:Bsas2012-modeling-eqsol3.png]]<br />
<br />
=== Transformed Model: SS===<br />
'''Steady states''' <br />
<br />
[[File:Bsas2012-modeling-soltrans.png]]<br />
<br />
The '''model''' obtained when m=j=1 differs:<br />
<br />
[[File:Bsas2012-modeling-modtrans1.png]]<br />
<br />
However the solutions remain the same.<br />
<br />
= References =</div>Abush84http://2012.igem.org/File:Montage-annotated.jpgFile:Montage-annotated.jpg2012-09-26T23:44:29Z<p>Abush84: </p>
<hr />
<div></div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-26T22:54:56Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1+(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
===== Steady State Solution =====<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (colors).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
== Model Analysis ==<br />
:The standard procedure when studying the temporal evolution of a non-linear system is to try to determine all the possible types of trajectories in the phase space. First the nullclines and fixed points (FP) are found; then the latter are classified according to their stability; in the same way as a linear system (**). The goal is to qualitatively draw the trajectories for any initial conditions to see how the variables evolve.<br />
<br />
Some relevant questions we can answer with this approach are:<br />
<br />
* How is this behavior governed by the model's parameters?<br />
<br />
* How do the trajectories change as the parameters vary? Does any new FP or close orbit appear?<br />
<br />
:There was a major difficulty in the analysis of the model because of the dimension of the problem - 4 variables. The analytical results yielded by the usual tools (nullclines, system linearization) could not be simplified so that all the relevant properties of the system could be understood in terms of the parameters. <br />
<br />
:We obtained the 4x4 Jacobian - evaluated in the FP - and later its eigenvalues. The expression found for them, a function of the relevant parameters, is too long and can't be simplified in a way that reveals changes in stability. <br />
<br />
:Besides that, we couldn't do a visual analysis of the nullclines to have a qualitative idea of what goes on.<br />
<br />
#A very common way to proceed in such a case is to reduce the model, ideally to a two-dimensional system. Sometimes one of the variables varies much more slowly/faster than the ones we are interested in, that it can be considered constant when studying the dynamics of the variables we are interested in. This is a standard procedure in the study of chemical reactions called Quasi Steady State Approximation. <br />
# We also noticed that the Hill coefficients are similar to one. So we explored the possibility of approximating them to one; is there a relevant change or not?<br />
<br />
:(**)Phase Plane review.<br />
<br />
===Numerical Simulations===<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
=== Model Reduction: Quasy Steady State Approximation ===<br />
<br />
:To gain some insight into the behavior of the model, we assumed that the variation of AA concentration in the medium is much faster than growth of the yeast populations. <br />
<br />
[[File:bsas2012-modeling-eq15.png]](6)<br />
<br />
: Therefore taking dAAj/dt=0, we re-wrote the Amino Acids as a function of ('''Na''' and '''Nb''') in a reduced 2x2 model for the evolution of the populations. <br />
<br />
[[File:bsas2012-modeling-eq16.png]](model_aprox)<br />
<br />
:Thus a phase portrait for the nullclines and trajectories was drawn. <br />
<br />
[[File:bsas2012-modeling-fignull.png]]<br />
<br />
Figure 2. Phase portrait for the reduce model.<br />
<br />
::Although the reduced model presents the same fixed point, it’s a saddle point. <br />
<br />
:What does that mean? Pick any point for the plane; those are your initial conditions for the concentration of each population. Now to see how the community evolves with time follow the arrows - like a particle in a flow. No matter how close to the FP we start, the community is overtaken by one of the two strains. <br />
<br />
* Only by taking i.c. along the stable manifold –direction marked between red arrows for clarification in Figure 2 – do we reach the FP we wanted as t &rarr; &infin;.<br />
* The stable FP are {Na &rarr; Nt, Nb &rarr; 0} and {Na &rarr; 0, Nb &rarr; Nt}. <br />
<br />
:This phase portrait is the one we'd expect for two ''species'' competing for the same resources. The ''Principle of Competitive Exclusion'' states that they can't typically coexist. <br />
<br />
::This is not a representation of auxotrophic community! Somehow we've landed on a Lotka-Volterra example.<br />
<br />
:The linearization of the set of ODEs around the FP yields instability regardless of the parameters. One of the eigenvalues is always positive. <br />
<br />
::{2death; death (1-√((pb pa)/(db da))/death)} <br />
<br />
:There is no agreement between the predictions found here and our simulations, that usually reach a non-trivial SS. We won’t pursue this reduction any further.<br />
<br />
=== Die or thrive?===<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
===Parameter Space and Solutions ===<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
====Conclusions====<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.<br />
<br />
====Effect of the Hill coeffient====<br />
<br />
:''How are the '''Kaa''' relevant when they haven't appear so far? ''<br />
<br />
::Originally we took both Hill coefficients equal to one in our numerical simulations, is close enough, right?<br />
<br />
:Well, the whole system didn't seem viable; it took over 100 hours for the fastest cultures to grow. Now using the fitted coefficients; the time scale becomes reasonable again.<br />
<br />
<br />
[[File:bsas2012-modeling-fig5.png]]<br />
<br />
Figure 5. Evolution in time of two cultures that only differ in the i.c.c. with both Hill coef = 1(left); and both Hill coef &gt; 1(right). <br />
<br />
* When n_ hill =1 only the culture with higher i.c.c. reaches a non-trivial SS. The other slowly dies. <br />
* There is no clear difference between the cases with high and low i.c.c. for n_ hill =1.5. Note that they reach SS faster as well.<br />
<br />
:The survival of an auxotrophic community is dependent on both the apparent dissociation constants (Ks) and the Hill coefficient as shown clearly on Figure 5. There are two effects related to Hill parameters ''K and n_hill'' here:<br />
<br />
Shorter time scale to SS.<br />
<br />
Larger subspace of i.c.c. that lead to SS.<br />
<br />
:So when creating an auxotrophy, pick a nutrient with an appropriate Hill coefficient and K. Check the ''range'' of allowed i.c.c. estimating the values of the rest of the parameters. We could say that the model is more stable as more sets of initial conditions are susceptible of regulation. <br />
<br />
:Our case is similar to the one shown in figure 5 with both hill coefficients &rarr; 1. Region III is less sensitive to variations in the i.c.c. and therefore the actual Region III resembles the theorical one in Figure 4. When n_hill=1 is much smaller.<br />
<br />
=== Relaxation time===<br />
<br />
:Not only are we interested in predicting the fraction of each strain in the culture, we need this control within a time frame or the system is irrelevant.<br />
<br />
:Let’s call &Tau; the time it takes one of the populations to reach its SS value (relaxation time). We rely again on numerical simulations to obtain those set of parameters where &Tau; is less than 65 hours, for example.<br />
<br />
:Creating a mesh we explored the parameter space (p, &epsilon;) for different initial conditions i.c.c. to visualize when the model’s ''prediction'' and the ''numerical result'' of the simulations for the '''mole fraction''' in SS differ in an amount lower than the given error; before 65 hours. We won’t limit the mesh to Region III only.<br />
<br />
::We’ve taken 5% of the value as error; considering it an accurate estimation of an experimental measure’s precision. <br />
<br />
::For each set of conditions ('''p''', &epsilon;,i.c.c.) we test the agreement between the proposed regulation and the NS.<br />
<br />
:The first two graphs in Figure 6 are different orientations in space of a '''binary test''', a '''blue dot''' indicates the points in this space where the error is smaller than 5%. Connecting the dots we see a pattern. This area in the parameter space where we can control the community through regulation has the same shape as Region III, although they don't overlap perfectly. <br />
<br />
:The '''green bubble's size''' in the third graph is a measure of the '''error''' in each point for the same NS; it ranges from 0.6 to 4%. The points with larger error are the ones in the area between II and III.<br />
<br />
:Some points well within Region III don't happen to comply with the established time limit. However there are enough points to see that all percentages are accessible with the model for the chosen precision. It's not necessary now, but a closer look at specific sections is possible with a higher point density matrix. <br />
<br />
[[File:bsas2012-modeling-fig6b.png]]<br />
<br />
[[File:bsas2012-modeling-fig6bi.png]]<br />
<br />
Figure 6. Scatter plot of the conditions under which the modeled auxotrophy leads to community control in under 65 hours with a 5% precision.<br />
<br />
=== Oscillations===<br />
<br />
:Damp oscillations are observed!!<br />
<br />
:This interesting effect was found while doing random searchs through our paramater space. It appears if:<br />
<br />
* the initial concentration of AA in the medium are of the order of the Ks and<br />
* the parameters that regulate the production and export of AA are from region II in Figure 4 and <br />
* we work with the approximate model where both n_hill= 1. <br />
<br />
[[File:bsas2012-modeling-fig7.png]]<br />
<br />
Figure 7. Evolution of the two populations in the community vs time when the conditions above are met. Both populations fluctuate with time in such a way that Nt remains constant. <br />
<br />
:We were unable to numerically find parameters where the oscillation period was shorter.<br />
<br />
== Model Transformation: a new analysis ==<br />
<br />
Let’s review what we’ve learned so far from our system.<br />
<br />
* Seems stable, <br />
<br />
* capable of oscillations, <br />
<br />
* there are two cases with a stable population and a defined mole fraction for each strain, one where the AAs are in SS and one where they aren’t. <br />
<br />
* The set of i.c. for which the culture thrives is dependent on the Ks. <br />
<br />
Yet all this information is not so trivial to find – especially the oscillations discovery was a fluke. <br />
<br />
There had to be a transformation where all these properties were more accessible or evident.<br />
<br />
What we did so far was to write the results in more convenient variables. <br />
<br />
Now we will try to find a transformation to a new set of variables, whose evolution is simpler to study and write a new set of ODEs for them.<br />
<br />
For instance the AAs only appear as the argument of Hill functions, so why not just work with those rations. We defined:<br />
<br />
[[File:bsas2012-modeling-eqt1.jpg]] (12)<br />
<br />
This is better because now &xi; is constant when:<br />
<br />
# AAa reachs steady state.<br />
<br />
# Na exports more that could Nb possibly adsorb and AAa accumulates in the medium. For AAi >> Ki the ratio &xi; &rarr; k_max.<br />
<br />
The same argument is valid for &mu;.<br />
<br />
Then we take the obvious choices to describe the percentage of each and the total population of the community. <br />
<br />
[[File:bsas2012-modeling-eqt2.jpg]] (13)<br />
<br />
Now we must find differential equations that describe their dynamic.<br />
<br />
For example:<br />
[[File:bsas2012-modeling-eqt3.jpg]] (14)<br />
<br />
and <br />
<br />
[[File:bsas2012-modeling-eqt4.jpg]] (15)<br />
<br />
The new set of ODEs with m,j >1 is:<br />
<br />
[[File:bsas2012-modeling-eqt6.jpg]] (transf_model)<br />
<br />
The model with m=j=1 is in the Appendix.<br />
<br />
* The AAs can be zero, so can the new variables. Rewriting the equation we see that each ratio is elevated to 2- (s+1)/s &gt; 0 for s &gt; 1; so the variables are not really in the denominator.<br />
<br />
* &chi; stop growing (is constant) if and only if &mu; = &xi;, n can’t equal 1 since n=1 doesn’t satisfy the DE for n.<br />
<br />
* This system has 4 solutions. The only physical one, is the one we already know: equation (3) in the old variables.<br />
<br />
* If &xi; = k_max, the culture again reaches a SS, but the fraction &chi; is indeterminate.<br />
<br />
Here’s the great improvement over the old variables. The SS for &chi;, &xi; and &mu; is independent of n; i.e. the conditions for which their DE equal zero. This means that the nullclines are independent as well. Now we have a reduction in the system's dimension. Same SS as 4x4 but this 3x3 system can be analyzed visually,<br />
<br />
* We can represent the vector field [[File:Bsas2012-modeling-eqt9.png]] in space and track a trajectory that starts at (&chi;0, &xi;0, &mu;0). <br />
* We can plot the nullclines, see where they meet; do a quick stability test by checking the sign of the vector field around the FP. <br />
<br />
[[File:bsas2012-modeling-fig9.jpg]]<br />
<br />
Figure 8. Nullclines for the reduced model in the new variables &chi;, &xi; and &mu;.<br />
<br />
=== Stability of the regulation. === <br />
<br />
Let’s directly go to the linearization of our 3 ODEs close to the FP. We said that n wasn’t relevant to the location of the fixed point, but now we have to consider it while examining the eigenvalues. <br />
<br />
Once again the expresion for the three eigenvalues &gamma; is very long. We replace the mayority of the parameters with the value we estimated, and n for its SS value - this is not always the case. <br />
<br />
We are in condition to study the stability of the FP when we vary p_a and p_b. The results are shown vs variables '''p''' and &epsilon; in figure 10. <br />
<br />
The FP is not stable if at least one of the eigenvalues is positive.<br />
<br />
[[File:bsas2012-modeling-fig10.jpg]]<br />
Figure 9. Visual aid to determine how the eigenvalue's sign changes with model's parameters '''p''' and &epsilon;.<br />
<br />
Since the three eigenvalues are negative, it's a stable node for this set of parameters. <br />
<br />
There are however some points for which there's no value plotted. Taking ('''p''', &epsilon;) from that region we obtain complex eigenvalues.<br />
<br />
==== Oscillations Revised====<br />
<br />
The oscillations in Figure 7 correspond to the model where the Hill coefficients are set to 1. Though we tried, we couldn't ''manually'' find a set of parameters and i.c. for which the model with the correct Hill coef. presents oscillations.<br />
<br />
Is this always the case? Could we find the answer using the new variables?<br />
<br />
We found a tendency by sampling random values '''p''',&epsilon; from the region where the eigenvalues are complex and i.c. &chi;0, &xi;0, &mu;0. The next example is representative of our findings:<br />
<br />
# &gamma;= {-0.0414142, -0.0351138 - 0.0715694 I, -0.0351138 + 0.0715694 I} with m,j =1<br />
<br />
# &gamma; with m,j >1<br />
<br />
There is a bifurcation that leads to stable trajectories in the second case; we haven't found it.<br />
<br />
The next figure shows some of the i.c. for which damp oscillations exist in the first case. <br />
<br />
[[File:bsas2012-modeling-fig11.jpg]]<br />
<br />
Figure 10. Oscillations in the fraction of each population when the AAs are initially saturated.<br />
<br />
Figure 10 presents the oscillations obtained for &chi;0 = 0.3, 3, 9 in black,red and blue. We varied the initial total population through n0= 1:2,1:20,1:200 for each &chi;0 and found little variance in such case.<br />
<br />
The more distintive characteristics of the oscillations are<br />
<br />
# AA must be initally saturated.<br />
# Huge period!<br />
# Dependance with the initial fraction &chi;0 but not on the intial dilution n0.<br />
<br />
== Parameter selection ==<br />
<br />
We've estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The Ks found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves OD vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|300px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations | experimental results]] and the best fit obtained for each are shown below in Figure 11.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 11. Single strain culture's OD after a number of hours vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The data's best fit is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] # Avog / (Molar mass AA), where [AA 1x] = 0.02 mg/ml<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml.<br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml.<br />
<br />
<br />
*The competition within a strain and with the other where taken as equal. The system's general ''carrying capacity'' taken in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 #cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given a top value (P_MAX) 1% of the maximum estimated number of elongation events for a yeast cell per hour. Our model does not explicitly include time as a variable, that means that the production is switched ON all the time. <br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
== Appendix ==<br />
<br />
=== Model : SS solution ===<br />
<br />
1. Nt -> 0<br />
<br />
2. [[File:Bsas2012-modeling-eqsol1.png]]<br />
<br />
3. [[File:Bsas2012-modeling-eqsol3.png]]<br />
<br />
=== Transformed Model: SS===<br />
'''Steady states''' <br />
<br />
[[File:Bsas2012-modeling-soltrans.png]]<br />
<br />
The '''model''' obtained when m=j=1 differs:<br />
<br />
[[File:Bsas2012-modeling-modtrans1.png]]<br />
<br />
However the solutions remain the same.<br />
<br />
= References =</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-26T22:53:48Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1+(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
===== Steady State Solution =====<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (colors).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
== Model Analysis ==<br />
:The standard procedure when studying the temporal evolution of a non-linear system is to try to determine all the possible types of trajectories in the phase space. First the nullclines and fixed points (FP) are found; then the latter are classified according to their stability; in the same way as a linear system (**). The goal is to qualitatively draw the trajectories for any initial conditions to see how the variables evolve.<br />
<br />
Some relevant questions we can answer with this approach are:<br />
<br />
* How is this behavior governed by the model's parameters?<br />
<br />
* How do the trajectories change as the parameters vary? Does any new FP or close orbit appear?<br />
<br />
:There was a major difficulty in the analysis of the model because of the dimension of the problem - 4 variables. The analytical results yielded by the usual tools (nullclines, system linearization) could not be simplified so that all the relevant properties of the system could be understood in terms of the parameters. <br />
<br />
:We obtained the 4x4 Jacobian - evaluated in the FP - and later its eigenvalues. The expression found for them, a function of the relevant parameters, is too long and can't be simplified in a way that reveals changes in stability. <br />
<br />
:Besides that, we couldn't do a visual analysis of the nullclines to have a qualitative idea of what goes on.<br />
<br />
#A very common way to proceed in such a case is to reduce the model, ideally to a two-dimensional system. Sometimes one of the variables varies much more slowly/faster than the ones we are interested in, that it can be considered constant when studying the dynamics of the variables we are interested in. This is a standard procedure in the study of chemical reactions called Quasi Steady State Approximation. <br />
# We also noticed that the Hill coefficients are similar to one. So we explored the possibility of approximating them to one; is there a relevant change or not?<br />
<br />
:(**)Phase Plane review.<br />
<br />
===Numerical Simulations===<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
=== Model Reduction: Quasy Steady State Approximation ===<br />
<br />
:To gain some insight into the behavior of the model, we assumed that the variation of AA concentration in the medium is much faster than growth of the yeast populations. <br />
<br />
[[File:bsas2012-modeling-eq15.png]](6)<br />
<br />
: Therefore taking dAAj/dt=0, we re-wrote the Amino Acids as a function of ('''Na''' and '''Nb''') in a reduced 2x2 model for the evolution of the populations. <br />
<br />
[[File:bsas2012-modeling-eq16.png]](model_aprox)<br />
<br />
:Thus a phase portrait for the nullclines and trajectories was drawn. <br />
<br />
[[File:bsas2012-modeling-fignull.png]]<br />
<br />
Figure 2. Phase portrait for the reduce model.<br />
<br />
::Although the reduced model presents the same fixed point, it’s a saddle point. <br />
<br />
:What does that mean? Pick any point for the plane; those are your initial conditions for the concentration of each population. Now to see how the community evolves with time follow the arrows - like a particle in a flow. No matter how close to the FP we start, the community is overtaken by one of the two strains. <br />
<br />
* Only by taking i.c. along the stable manifold –direction marked between red arrows for clarification in Figure 2 – do we reach the FP we wanted as t &rarr; &infin;.<br />
* The stable FP are {Na &rarr; Nt, Nb &rarr; 0} and {Na &rarr; 0, Nb &rarr; Nt}. <br />
<br />
:This phase portrait is the one we'd expect for two ''species'' competing for the same resources. The ''Principle of Competitive Exclusion'' states that they can't typically coexist. <br />
<br />
::This is not a representation of auxotrophic community! Somehow we've landed on a Lotka-Volterra example.<br />
<br />
:The linearization of the set of ODEs around the FP yields instability regardless of the parameters. One of the eigenvalues is always positive. <br />
<br />
::{2death; death (1-√((pb pa)/(db da))/death)} <br />
<br />
:There is no agreement between the predictions found here and our simulations, that usually reach a non-trivial SS. We won’t pursue this reduction any further.<br />
<br />
=== Die or thrive?===<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
===Parameter Space and Solutions ===<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilon; according to the formula on equation (5). The total population of the community (Nt) is the same as in Region II.<br />
<br />
::'''This is the region where the auxotrophy leads to regulation of the community.''' However the culture doesn’t grow for all i.c.c. – will get back to this point shortly.<br />
<br />
====Conclusions====<br />
::Some conclusions we've drawn from these numerical simulations:<br />
<br />
# We had postulated that the solution doesn't hold in region II and expected a dramatic change in the system’s behavior. However there is a smooth transition between regions II and III. For a fixed &epsilon; we can go from region III to region II by increasing '''p'''. Once we cross the threshold we notice that the total population of cells (Nt) doeen't vary from one region to the next. Though the AA continues to grow over time (and accumulate) it does so slowly; more so than we'd expect for a vertical asymptote. <br />
# More so, the formula (5) is a good approximation for the fraction of each strain in the region II close to the limit with region III; the difference between the two increases gradually as '''p''' gets further away from this threshold value. The formula is not true in II; thus regulation fails.<br />
# There is no dramatic change caused by the asymptote, in fact the two regions can't be distinguished experimentally through a strain's fraction measurement in each side of the frontier; so small is the difference between the values across. <br />
# Taking into account that the threshold is based on estimations; perhaps is safer to choose points well inside Region III to ensure proper regulation. This has a cost, for a fixed percentage the production and export of AAs is now lower and the dynamic of the culture slower.<br />
<br />
:The original purpose of this analysis was to undertand '''under which initial conditions do the populations thrive or die'''. This has to be analyzed in terms of Figure 4, not merely the conditions for &lambda;.<br />
<br />
:We noticed that the culture's survival in region III also depends on the values of '''Kaa''', even though is not explicit in any formula so far. Remember that '''Kaa''' is the concentration of an AA in the medium required for half maximal growth rate. For a fixed value of '''Kaa''' there is a critical initial concentration of cells below which the culture doesn't prosper. This effect is important when the percentage desired is extreme (close 100%), where the '''p''' value required for the strain gets really low. <br />
<br />
:This is reasonable considering that another time scale is introduced. Not only have the cells to produce and absorb the required amounts of AA before they die, they also need to modify the AA concentration of the medium. The cells must produce enough amino acids so that the concentration of AA approximates '''Kaa'', before the original cells die out. If the initial cell density is to low, this won't happen.<br />
<br />
====Effect of the Hill coeffient====<br />
<br />
:''How are the '''Kaa''' relevant when they haven't appear so far? ''<br />
<br />
::Originally we took both Hill coefficients equal to one in our numerical simulations, is close enough, right?<br />
<br />
:Well, the whole system didn't seem viable; it took over 100 hours for the fastest cultures to grow. Now using the fitted coefficients; the time scale becomes reasonable again.<br />
<br />
<br />
[[File:bsas2012-modeling-fig5.png]]<br />
<br />
Figure 5. Evolution in time of two cultures that only differ in the i.c.c. with both Hill coef = 1(left); and both Hill coef &gt; 1(right). <br />
<br />
* When n_ hill =1 only the culture with higher i.c.c. reaches a non-trivial SS. The other slowly dies. <br />
* There is no clear difference between the cases with high and low i.c.c. for n_ hill =1.5. Note that they reach SS faster as well.<br />
<br />
:The survival of an auxotrophic community is dependent on both the apparent dissociation constants (Ks) and the Hill coefficient as shown clearly on Figure 5. There are two effects related to Hill parameters ''K and n_hill'' here:<br />
<br />
Shorter time scale to SS.<br />
<br />
Larger subspace of i.c.c. that lead to SS.<br />
<br />
:So when creating an auxotrophy, pick a nutrient with an appropriate Hill coefficient and K. Check the ''range'' of allowed i.c.c. estimating the values of the rest of the parameters. We could say that the model is more stable as more sets of initial conditions are susceptible of regulation. <br />
<br />
:Our case is similar to the one shown in figure 5 with both hill coefficients &rarr; 1. Region III is less sensitive to variations in the i.c.c. and therefore the actual Region III resembles the theorical one in Figure 4. When n_hill=1 is much smaller.<br />
<br />
=== Relaxation time===<br />
<br />
:Not only are we interested in predicting the fraction of each strain in the culture, we need this control within a time frame or the system is irrelevant.<br />
<br />
:Let’s call &Tau; the time it takes one of the populations to reach its SS value (relaxation time). We rely again on numerical simulations to obtain those set of parameters where &Tau; is less than 65 hours, for example.<br />
<br />
:Creating a mesh we explored the parameter space (p, &epsilon;) for different initial conditions i.c.c. to visualize when the model’s ''prediction'' and the ''numerical result'' of the simulations for the '''mole fraction''' in SS differ in an amount lower than the given error; before 65 hours. We won’t limit the mesh to Region III only.<br />
<br />
::We’ve taken 5% of the value as error; considering it an accurate estimation of an experimental measure’s precision. <br />
<br />
::For each set of conditions ('''p''', &epsilon;,i.c.c.) we test the agreement between the proposed regulation and the NS.<br />
<br />
:The first two graphs in Figure 6 are different orientations in space of a '''binary test''', a '''blue dot''' indicates the points in this space where the error is smaller than 5%. Connecting the dots we see a pattern. This area in the parameter space where we can control the community through regulation has the same shape as Region III, although they don't overlap perfectly. <br />
<br />
:The '''green bubble's size''' in the third graph is a measure of the '''error''' in each point for the same NS; it ranges from 0.6 to 4%. The points with larger error are the ones in the area between II and III.<br />
<br />
:Some points well within Region III don't happen to comply with the established time limit. However there are enough points to see that all percentages are accessible with the model for the chosen precision. It's not necessary now, but a closer look at specific sections is possible with a higher point density matrix. <br />
<br />
[[File:bsas2012-modeling-fig6b.png]]<br />
<br />
[[File:bsas2012-modeling-fig6bi.png]]<br />
<br />
Figure 6. Scatter plot of the conditions under which the modeled auxotrophy leads to community control in under 65 hours with a 5% precision.<br />
<br />
=== Oscillations===<br />
<br />
:Damp oscillations are observed!!<br />
<br />
:This interesting effect was found while doing random searchs through our paramater space. It appears if:<br />
<br />
* the initial concentration of AA in the medium are of the order of the Ks and<br />
* the parameters that regulate the production and export of AA are from region II in Figure 4 and <br />
* we work with the approximate model where both n_hill= 1. <br />
<br />
[[File:bsas2012-modeling-fig7.png]]<br />
<br />
Figure 7. Evolution of the two populations in the community vs time when the conditions above are met. Both populations fluctuate with time in such a way that Nt remains constant. <br />
<br />
:We were unable to numerically find parameters where the oscillation period was shorter.<br />
<br />
== Model Transformation: a new analysis ==<br />
<br />
Let’s review what we’ve learned so far from our system.<br />
<br />
* Seems stable, <br />
<br />
* capable of oscillations, <br />
<br />
* there are two cases with a stable population and a defined mole fraction for each strain, one where the AAs are in SS and one where they aren’t. <br />
<br />
* The set of i.c. for which the culture thrives is dependent on the Ks. <br />
<br />
Yet all this information is not so trivial to find – especially the oscillations discovery was a fluke. <br />
<br />
There had to be a transformation where all these properties were more accessible or evident.<br />
<br />
What we did so far was to write the results in more convenient variables. <br />
<br />
Now we will try to find a transformation to a new set of variables, whose evolution is simpler to study and write a new set of ODEs for them.<br />
<br />
For instance the AAs only appear as the argument of Hill functions, so why not just work with those rations. We defined:<br />
<br />
[[File:bsas2012-modeling-eqt1.jpg]] (12)<br />
<br />
This is better because now &xi; is constant when:<br />
<br />
# AAa reachs steady state.<br />
<br />
# Na exports more that could Nb possibly adsorb and AAa accumulates in the medium. For AAi >> Ki the ratio &xi; &rarr; k_max.<br />
<br />
The same argument is valid for &mu;.<br />
<br />
Then we take the obvious choices to describe the percentage of each and the total population of the community. <br />
<br />
[[File:bsas2012-modeling-eqt2.jpg]] (13)<br />
<br />
Now we must find differential equations that describe their dynamic.<br />
<br />
For example:<br />
[[File:bsas2012-modeling-eqt3.jpg]] (14)<br />
<br />
and <br />
<br />
[[File:bsas2012-modeling-eqt4.jpg]] (15)<br />
<br />
The new set of ODEs with m,j >1 is:<br />
<br />
[[File:bsas2012-modeling-eqt6.jpg]] (transf_model)<br />
<br />
The model with m=j=1 is in the Appendix.<br />
<br />
* The AAs can be zero, so can the new variables. Rewriting the equation we see that each ratio is elevated to 2- (s+1)/s &gt; 0 for s &gt; 1; so the variables are not really in the denominator.<br />
<br />
* &chi; stop growing (is constant) if and only if &mu; = &xi;, n can’t equal 1 since n=1 doesn’t satisfy the DE for n.<br />
<br />
* This system has 4 solutions. The only physical one, is the one we already know: equation (3) in the old variables.<br />
<br />
* If &xi; = k_max, the culture again reaches a SS, but the fraction &chi; is indeterminate.<br />
<br />
Here’s the great improvement over the old variables. The SS for &chi;, &xi; and &mu; is independent of n; i.e. the conditions for which their DE equal zero. This means that the nullclines are independent as well. Now we have a reduction in the system's dimension. Same SS as 4x4 but this 3x3 system can be analyzed visually,<br />
<br />
* We can represent the vector field [[File:Bsas2012-modeling-eqt9.png]] in space and track a trajectory that starts at (&chi;0, &xi;0, &mu;0). <br />
* We can plot the nullclines, see where they meet; do a quick stability test by checking the sign of the vector field around the FP. <br />
<br />
[[File:bsas2012-modeling-fig9.jpg]]<br />
<br />
Figure 8. Nullclines for the reduced model in the new variables &chi;, &xi; and &mu;.<br />
<br />
=== Stability of the regulation. === <br />
<br />
Let’s directly go to the linearization of our 3 ODEs close to the FP. We said that n wasn’t relevant to the location of the fixed point, but now we have to consider it while examining the eigenvalues. <br />
<br />
Once again the expresion for the three eigenvalues &gamma; is very long. We replace the mayority of the parameters with the value we estimated, and n for its SS value - this is not always the case. <br />
<br />
We are in condition to study the stability of the FP when we vary p_a and p_b. The results are shown vs variables '''p''' and &epsilon; in figure 10. <br />
<br />
The FP is not stable if at least one of the eigenvalues is positive.<br />
<br />
[[File:bsas2012-modeling-fig10.jpg]]<br />
Figure 9. Visual aid to determine how the eigenvalue's sign changes with model's parameters '''p''' and &epsilon;.<br />
<br />
Since the three eigenvalues are negative, it's a stable node for this set of parameters. <br />
<br />
There are however some points for which there's no value plotted. Taking ('''p''', &epsilon;) from that region we obtain complex eigenvalues.<br />
<br />
==== Oscillations Revised====<br />
<br />
The oscillations in Figure 7 correspond to the model where the Hill coefficients are set to 1. Though we tried, we couldn't ''manually'' find a set of parameters and i.c. for which the model with the correct Hill coef. presents oscillations.<br />
<br />
Is this always the case? Could we find the answer using the new variables?<br />
<br />
We found a tendency by sampling random values '''p''',&epsilon; from the region where the eigenvalues are complex and i.c. &chi;0, &xi;0, &mu;0. The next example is representative of our findings:<br />
<br />
# &gamma;= {-0.0414142, -0.0351138 - 0.0715694 I, -0.0351138 + 0.0715694 I} with m,j =1<br />
<br />
# &gamma; with m,j >1<br />
<br />
There is a bifurcation that leads to stable trajectories in the second case; we haven't found it.<br />
<br />
The next figure shows some of the i.c. for which damp oscillations exist in the first case. <br />
<br />
[[File:bsas2012-modeling-fig11.jpg]]<br />
<br />
Figure 10. Oscillations in the fraction of each population when the AAs are initially saturated.<br />
<br />
Figure 10 presents the oscillations obtained for &chi;0 = 0.3, 3, 9 in black,red and blue. We varied the initial total population through n0= 1:2,1:20,1:200 for each &chi;0 and found little variance in such case.<br />
<br />
The more distintive characteristics of the oscillations are<br />
<br />
# AA must be initally saturated.<br />
# Huge period!<br />
# Dependance with the initial fraction &chi;0 but not on the intial dilution n0.<br />
<br />
== Parameter selection ==<br />
<br />
We've estimatated values for all the parameter in the model to check its viability.<br />
<br />
*The Ks found in the equations are related to the concentration of AAs in the medium required to reach half the maximum rate of growth. Curves OD vs [AA](t=0) were measured experimentally for each amino acid and the data fitted to a Hill equation (16) using MATLAB’s TOOLBOX: Curve Fitting Tool.<br />
<br />
[[File:Bsas2012-modeling-eqfit.png|300px]](16)<br />
<br />
This data from [[Team:Buenos_Aires/Results/Strains#Growth dependence on the Trp and His concentrations]] and the best fit obtained for each are shown below on Figure 11.<br />
<br />
[[File:Bsas2012-modeling-fig_aux.png]]<br />
<br />
Figure 11. Single strain culture's OD after a number of hours vs the initial concentration of AA in the medium (dilutions 1:X).<br />
The data's best fit is also shown.<br />
<br />
The values taken from the fits are <br />
<br />
His- :<br />
K_dil = 0.3279 <br />
ODmax = 62.53 <br />
n = 1.243<br />
R-square: 0.9376 <br />
<br />
Trp- :<br />
K_dil = 0.1770<br />
ODmax = 57.46 <br />
n = 1.636 <br />
R-square: 0.9905<br />
<br />
<br />
<br />
The results were then converted to the units chosen for the simulations.<br />
<br />
K = K_dil * [AA 1x] # Avog / (Molar mass AA), where [AA 1x] = 0.02 mg/ml<br />
<br />
**K trp= 0.1770 * 5.88e16 AA / ml.<br />
<br />
**K his= 0.3279 * 7.8e16 AA / ml.<br />
<br />
<br />
*The competition within a strain and with the other where taken as equal. The system's general ''carrying capacity'' taken in the model is the one often used here[ref], in a lab that works with these yeast strains:<br />
<br />
**Cc= 3e7 #cell/ml.<br />
<br />
<br />
*The death rate was taken between 3 and 7 days.<br />
<br />
<br />
*The rate of “production and export” was given a top value (P_MAX) 1% of the maximum estimated number of elongation events for a yeast cell per hour. Our model does not explicitly include time as a variable, that means that the production is switched ON all the time. <br />
<br />
Elongation events (peptidil transferase reactions): 6e6 1 / cell sec [ref]<br />
<br />
P_MAX = 2.16e8 1/ cell hour <br />
<br />
These parameters will be regulated experimentally (&epsilon; , '''p''') to alter the fraction between populations.<br />
<br />
**p trp = '''p'''* '''&epsilon;'''*2.16e8 AA / cell hour<br />
<br />
**p his = '''p'''*2.16e8 AA / cell hour<br />
<br />
where '''p''' < 1 controls the fraction of the top value and &epsilon; the ratio between the export of each amino acid.<br />
<br />
<br />
*'''d''' is the total number of amino acids in a yeast cell –same as the ones needed to create a daughter -per cell: <br />
<br />
'''d''' = # A.A. per cell = (mass of protein per yeast cell [ref]) * relative abundance of AA * # Avog / AA's molar mass.<br />
<br />
<br />
**d trp= 2.630e7 AA / cell <br />
<br />
**d his= 6.348e8 AA / cell<br />
<br />
== Appendix ==<br />
<br />
=== Model : SS solution ===<br />
<br />
1. Nt -> 0<br />
<br />
2. [[File:Bsas2012-modeling-eqsol1.png]]<br />
<br />
3. [[File:Bsas2012-modeling-eqsol3.png]]<br />
<br />
=== Transformed Model: SS===<br />
'''Steady states''' <br />
<br />
[[File:Bsas2012-modeling-soltrans.png]]<br />
<br />
The '''model''' obtained when m=j=1 differs:<br />
<br />
[[File:Bsas2012-modeling-modtrans1.png]]<br />
<br />
However the solutions remain the same.<br />
<br />
= References =</div>Abush84http://2012.igem.org/Team:Buenos_Aires/Project/ModelTeam:Buenos Aires/Project/Model2012-09-26T22:51:21Z<p>Abush84: /* Parameter selection */</p>
<hr />
<div>{{:Team:Buenos_Aires/Templates/menu}}<br />
<br />
= Modelling a synthetic ecology =<br />
To gain insight into the behavior of our crossfeeding design, to understand which parameters of the system are important, and to study the feasibility of the project, we decided to implement a model of the system. We found this interesting per se, as the model we need is that of a microbial community and ecology. <br />
<br />
The mathematical modeling of ecological system is an active field of research with a very rich and interesting history; from the works of Lotka and Volterra, who modeled predator-prey dynamics with ordinary differential equations, to Robert May’s chaotic logistic maps. In fact a lot of synthetic systems of interacting bacteria created in recent years were used to study these sort of models.<br />
<br />
== The crossfeeding model ==<br />
<br />
In the crossfeeding design, each strain produces and releases to the medium an aminoacid the other strain(s) need to grow. The growth rate of each strain depends on the aminoacid (AA) concentration of those AA it can’t produce. In turn these concentrations depend on the abundance of the other strains. Therefore the growth of the strains is interdependent.<br />
<br />
For simplicity and to be consistent with the experimental work, we decided to model two interacting strains, one that produces tryptophan (Trp) but requires histidine (His), and another that produces His but requires Trp. <br />
<br />
We decided to use ordinary differential equations to model the system. This is a normal approach when studying population dynamics of this kind. <br />
<br />
The model has four variables:<br />
*[Nhis-] : the concentration of histidine dependent (Trp producing) yeast cells, in cells per ml<br />
*[Ntrp-] : the total amount of tryptophan dependent (His producing) yeast cells, in cell per ml<br />
*[his]: the concentration of histidine in the medium, in molecules per ml<br />
*[trp]: the concentration of tryptophan in the medium, in molecules per ml.<br />
<br />
To build the model we did the following assumptions:<br />
* The growth rate of each strain depends on the concentration in the medium of the AA they can’t produce. As the concentration of this AA in the medium increases, so does the growth rate of the strain, until it settles at the growth rate observed in optimal conditions (doubling time of 90 min for yeast).<br />
* There is a maximal density of cells the medium can support (carrying capacity)<br />
* Each cell has a fixed probability of dying per time interval<br />
* Each cell releases to the medium the AA it produces at a fixed rate<br />
* Each strain only consumes the AA it doesn't produce<br />
* The system reaches steady state.<br />
<br />
[[File:Bsas2012-model1.png]] (model)<br />
<br />
In the equation for the temporal evolution of each population we take the growth rate as a Hill function of the amount of AA to which the strain is auxotrophic (it can't produce). The function captures the increase of the growth rate with the relevant AA concentration, until it reaches a plateau at the maximal growth rate, that corresponds to a doubling time of 90 minutes (doubling time = ln(2)/kmax). These functions are of the form<br />
<br />
[[File:bsas2012-modeling-eq1.png|200px]](1)<br />
<br />
where '''Kaa''' is the effective concentration of AA at which half maximal growth rate is obtained, and '''l''' is the Hill coefficient, that describes how "steep" the curve is. <br />
<br />
The term (1+(Nhis+Ntrp)/Cc) of the model accounts for other factors, not explicit in our model that can limit the concentration of cells in a given volume (carrying capacity), enabling the population to reach equilibrium. We included a term related to cell death (- death Ntrp), which is makes biologically sense (cell do die) and is critical for a correct behavior of the model.<br />
<br />
The terms in the equations for the evolution of each [AA] in the medium are the fluxes of AA entering and leaving the medium. The first term (Ptrp*Nhis-) is a measure of the AA produced and exported in the form of peptides by a cell, for example the rate of tryptophan secretion by the histidine dependent cell. <br />
<br />
The second term is the flux leaving the medium and entering the auxotrophic cells. Each cell has a more or less fixed amount of each amino acid, which we call '''d'''. If a cell is to replicate itself and cannot produce and amino acid, it will have to absorb it from the medium. Therefore the flux of AA entering the cell, is '''d''' divided by the doubling time &tau; (the time it takes to "construct" a new cell). <br />
One of the hypotheses built into our model is that &tau; will vary greatly with the concentration of nutrients available. We've used <br />
<br />
[[File:bsas2012-modeling-eq2.png|200px]](2)<br />
<br />
The amount of AA entering the cells that produce it was considered negligible. This means that a cell that produces Trp doesn't import it from the medium in significant quantities. This is likely to hold when AA concentrations are limiting. <br />
<br />
===== Steady State Solution =====<br />
<br />
The 4 equations in the model were equaled to zero and the non-linear algebraic system solved using [http://www.wolfram.com/mathematica/ Mathematica] to find their equilibrium values.<br />
<br />
Here we change the notation a little bit to two generic strains '''a''' and '''b''' where population '''Na''' produces amino acid '''a''' and requires '''b''', and population '''Nb''' produces '''b''' and requires '''a'''. We are interested in the value of the cell populations in equilibrium, or more importantly the fraction of each population in steady state. Thus a change was made to more convenient variables: the sum of both population and a strain's fraction.<br />
<br />
Nt = Na + Nb<br />
<br />
Xa = Na / Nt<br />
<br />
Only 3 fixed points were found for the system (See [[Team:Buenos_Aires/Project/Model#Appendix | Appendix]]). There are some solutions for which it isn't true that the four variables reach a steady state (SS) or equilibrium. There are initial conditions for which the AA concentrations will not stop growing! So caution is required when assuming steady state. <br />
<br />
*In the first solution we found the culture doesn't thrive, for example because one strain was missing or the initial density was to low.<br />
<br />
*The second one has no biological relevance since it yields negative concentrations for the Amino Acids.<br />
<br />
*The third one is the significant one (see equations below). However for some parameters the concentrations of Nt and AA can be negative, therefore restricting the parameter space in which the model works. <br />
<br />
<br />
[[File:bsas2012-modeling-eqsol3.png]](3)<br />
<br />
=== Regulation ===<br />
<br />
Note that the fraction of each strain in the community is a function of the AA secretion rates ('''pa''' and '''pb''') and the amount of AA required to "construct" a cell ('''da''' and '''db''').<br />
<br />
[[File:bsas2012-modeling-eq4.png]] (4)<br />
<br />
For positive values of '''d''' and '''p''' the range of the function is (0; 1), consistent with what we expect for a fraction.<br />
<br />
The fraction of each strain in the culture in equilibrium are regulated by the production and export of each AA – represented in the model through '''pa''' and '''pb'''. These fractions are independent of initial conditions! This means that the system auto-regulates itself, as intended. <br />
<br />
In fact we only need to control the ratio between these parameters to control the culture composition: <br />
<br />
[[File:bsas2012-modeling-eq5.png]](5)<br />
<br />
The next figure illustrates how the fraction Xa varies with the variable &epsilon;.<br />
<br />
[[File:bsas2012-modeling-fig1.png]]<br />
<br />
Figure 1. Fraction Xa as a fuction of the ratio of protein export for values of D=0.1,1,10 (colors).<br />
<br />
To programme the percentage we need a second set of biobricks that can sense an external stimulus and transduce this signal to modify &epsilon;. The range of percentages we can control is determined by the range of &epsilon; accessible with this second device and the ratio D that is set for any two A.A.<br />
<br />
=== Parameters ===<br />
<br />
The parameter estimation process is detailed in [[Team:Buenos_Aires/Project/Model#Parameter_selection | parameter selection]]<br />
<br />
{| class="wikitable"<br />
|+ align="top" style="color:#e76700;" |''Parameters selected''<br />
|-<br />
|<br />
|style="color:white; background-color: purple;"|'''Value'''<br />
|style="color:white; background-color: purple;"|'''Units'''<br />
|-<br />
|style="color:white; background-color: purple;"|kmax<br />
|0.4261<br />
|1/hr<br />
|-<br />
|style="color:white; background-color: purple;"|K his<br />
|2.588e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|K trp<br />
|1.041e16<br />
|AA/ml<br />
|-<br />
|style="color:white; background-color: purple;"|n his<br />
|1.243<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|n trp<br />
|1.636<br />
|<br />
|-<br />
|style="color:white; background-color: purple;"|Cc<br />
|3.0 10^7<br />
|cell/ml<br />
|-<br />
|style="color:white; background-color: purple;"|death<br />
|3 to 7<br />
|days<br />
|-<br />
|style="color:white; background-color: purple;"|p his<br />
|p 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|p trp<br />
|p &epsilon; 2.16e8<br />
|AA/ cell hr<br />
|-<br />
|style="color:white; background-color: purple;"|d his<br />
|6.348e8<br />
|AA/cell<br />
|-<br />
|style="color:white; background-color: purple;"|d trp<br />
|2.630e7 <br />
|AA/cell<br />
|}<br />
<br />
Table 1. Model parameters used for the simulations<br />
<br />
== Model Analysis ==<br />
:The standard procedure when studying the temporal evolution of a non-linear system is to try to determine all the possible types of trajectories in the phase space. First the nullclines and fixed points (FP) are found; then the latter are classified according to their stability; in the same way as a linear system (**). The goal is to qualitatively draw the trajectories for any initial conditions to see how the variables evolve.<br />
<br />
Some relevant questions we can answer with this approach are:<br />
<br />
* How is this behavior governed by the model's parameters?<br />
<br />
* How do the trajectories change as the parameters vary? Does any new FP or close orbit appear?<br />
<br />
:There was a major difficulty in the analysis of the model because of the dimension of the problem - 4 variables. The analytical results yielded by the usual tools (nullclines, system linearization) could not be simplified so that all the relevant properties of the system could be understood in terms of the parameters. <br />
<br />
:We obtained the 4x4 Jacobian - evaluated in the FP - and later its eigenvalues. The expression found for them, a function of the relevant parameters, is too long and can't be simplified in a way that reveals changes in stability. <br />
<br />
:Besides that, we couldn't do a visual analysis of the nullclines to have a qualitative idea of what goes on.<br />
<br />
#A very common way to proceed in such a case is to reduce the model, ideally to a two-dimensional system. Sometimes one of the variables varies much more slowly/faster than the ones we are interested in, that it can be considered constant when studying the dynamics of the variables we are interested in. This is a standard procedure in the study of chemical reactions called Quasi Steady State Approximation. <br />
# We also noticed that the Hill coefficients are similar to one. So we explored the possibility of approximating them to one; is there a relevant change or not?<br />
<br />
:(**)Phase Plane review.<br />
<br />
===Numerical Simulations===<br />
:Initially we relied on numerical simulations (NS) to explore possible behaviors of the model.<br />
<br />
:To run the simulations all that is left is to choose values {p, &epsilon;, initial conditions}.<br />
<br />
* Since this is merely a framewok we have taken values '''p''' from a log scale from -3 to 1. <br />
* '''&epsilon;''' was ranged from 0.0001 to 4; which varies the fraction of each population from 10% to 90%. <br />
* Cells initial concentration (i.c.c.): dilutions from 1:10000 to 10x of the carrying capacity (Cc). <br />
* Amino acids (AA) initial concentration: null, unless stated otherwise.<br />
<br />
=== Model Reduction: Quasy Steady State Approximation ===<br />
<br />
:To gain some insight into the behavior of the model, we assumed that the variation of AA concentration in the medium is much faster than growth of the yeast populations. <br />
<br />
[[File:bsas2012-modeling-eq15.png]](6)<br />
<br />
: Therefore taking dAAj/dt=0, we re-wrote the Amino Acids as a function of ('''Na''' and '''Nb''') in a reduced 2x2 model for the evolution of the populations. <br />
<br />
[[File:bsas2012-modeling-eq16.png]](model_aprox)<br />
<br />
:Thus a phase portrait for the nullclines and trajectories was drawn. <br />
<br />
[[File:bsas2012-modeling-fignull.png]]<br />
<br />
Figure 2. Phase portrait for the reduce model.<br />
<br />
::Although the reduced model presents the same fixed point, it’s a saddle point. <br />
<br />
:What does that mean? Pick any point for the plane; those are your initial conditions for the concentration of each population. Now to see how the community evolves with time follow the arrows - like a particle in a flow. No matter how close to the FP we start, the community is overtaken by one of the two strains. <br />
<br />
* Only by taking i.c. along the stable manifold –direction marked between red arrows for clarification in Figure 2 – do we reach the FP we wanted as t &rarr; &infin;.<br />
* The stable FP are {Na &rarr; Nt, Nb &rarr; 0} and {Na &rarr; 0, Nb &rarr; Nt}. <br />
<br />
:This phase portrait is the one we'd expect for two ''species'' competing for the same resources. The ''Principle of Competitive Exclusion'' states that they can't typically coexist. <br />
<br />
::This is not a representation of auxotrophic community! Somehow we've landed on a Lotka-Volterra example.<br />
<br />
:The linearization of the set of ODEs around the FP yields instability regardless of the parameters. One of the eigenvalues is always positive. <br />
<br />
::{2death; death (1-√((pb pa)/(db da))/death)} <br />
<br />
:There is no agreement between the predictions found here and our simulations, that usually reach a non-trivial SS. We won’t pursue this reduction any further.<br />
<br />
=== Die or thrive?===<br />
:Some basic properties were observed by simplifying the system; we wanted to check that our model was sound. We considered a "symmetric interaction" in which both secretion rates and amino acid requirement were equivalent. <br />
<br />
[[File:bsas2012-modeling-eq6.png]](8)<br />
<br />
where we can define <br />
<br />
[[File:bsas2012-modeling-eq7.png]] (9)<br />
<br />
:This parameter &lambda; has an intuitive interpretation. The ratio '''d'''/'''p''' is the time it takes a cell to export enough AA for a cell of the other strain to build a daughter. On the other hand, 1/death is the average life span of a cell. Therefore &lambda; is the ratio between the "life span" of a cell and the time it takes to export sufficient AA to build a cell, thus &lambda; represents the amount of cells that can be constructed with the AA secreted by a cell in its lifetime. If this value is grated than one (&lambda; &gt; 1) the culture grows, otherwise it dies (This is completely analogous to the "force of infection" as defined in epidemiology). <br />
<br />
:Keeping '''d''' and '''death''' constant, the total steady state number of cells in the culture depends on the secretion rate '''p''' as shown in the following figure 3. There is a threshold value p given by &lambda;=1; with lower values the culture dies.<br />
<br />
[[File:bsas2012-modeling-fig3.5.png]] <br />
<br />
Figure 3. Total number of cells in the mix vs the strengh '''p''' of the production and export of AAs. <br />
<br />
<br />
<br />
:Another condition is related to extra-cellular concentration of amino acids. Looking at the equations for the evolution of the AA in the model, we can identify the parameter &delta; as follows<br />
<br />
[[File:bsas2012-modeling-eq8.png]](10)<br />
<br />
: As before, the ratio '''&radic;(da db)/&radic;(pa pb)''' is the average time required for a cell to export enough AA to construct another cell. '''1/kmax''' is the time it takes a cell to replicate in optimal growth conditions. &delta; can be interpret as the amount of cells that can be made with the material secreted a by a single cell before it divides (in optimal conditions). If &delta; &gt; 1 the amount of nutrients produced exceeds the consumption, the medium gets saturated with AA and the regulation fails. So the system is auto-regulated only if &delta; &lt; 1.<br />
<br />
:Combining these two conditions (&lambda; &gt; 1 and &delta; &lt; 1), we conclude that the production rate has to be in a defined region for the system to work. To low and the culture dies out, to big and it gets out of control.<br />
<br />
===Parameter Space and Solutions ===<br />
:Uniting the conditions for &lambda; and &delta; we see that in general '''p''' and &epsilon; must be bound for our solution to make sense (i.e. all concentrations &gt; 0):<br />
<br />
[[File:bsas2012-modeling-eq14.png]](11)<br />
<br />
<br />
[[File:bsas2012-modeling-fig3.png]]<br />
Figure 4. Regions in the parameter space that present different types of solutions. Only in Region III the system works as intended.<br />
<br />
<br />
:Now, let's classify these regions numerically to see whether the ideas we just presented are supported. Taking random values from each region we found that all four variables are always positive, but each region is associated with a different kind of solution. The conditions shape the behavior and not the existence of a solution.<br />
<br />
:Taking AA(t=0)=0:<br />
<br />
::I. The culture doesn't grow, it decays with varying velocities for any initial concentration of cells (i.c.c.). This is consistent with solution 1, where Nt= 0 as t &rarr; &infin;. This is the &lambda; &lt; 1 case.<br />
<br />
<br />
::II. The extracellular AA concentration keeps growing and the medium gets saturated. The culture reaches equilibrium for every i.c.c. no matter how low, but there is no regulation of the composition of the culture. This is the &delta; &gt; 1 case. <br />
<br />
::The growth rate tends to the theoretical 90 min and the AA dependence disappears from the equations for each population. Remarkably the fraction of each strain is still independent of the i.c.c.<br />
<br />
<br />
::III. The four variables reach SS. The mole fraction is solely dependent on &epsilo