Team:British Columbia/Consortia

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<font face=arial narrow size=4><b>Modeling Microbial Consortia: The Auxotroph Approach</b></font></br><font face=arial narrow></html>
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<font face=arial narrow size=5><b>Modeling Microbial Consortia: The Auxotroph Approach</b></font></br><font face=arial narrow>
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https://static.igem.org/mediawiki/2012/1/1f/Model_animation.swf
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There is a large potential combinatorial space for optimizing and tuning a three-member microbial consortium with auxotrophic interdependence. In order to help address this concern, we set to develop a computational framework to help guide experimental decisions. <b>Our model attempts to develop a predictive understanding of auxotrophic interdependence in a three-member microbial consortium.</b> As it was important for us to be able to predict growth kinetics based on measurable, empirical properties, as well as to develop a mathematically and experimentally tractable system, we decided to base our model on Monod kinetics. </br></br>
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To gain a predictive understanding of consortium dynamics in the wet lab, we chose to model a system of simple auxotrophic interdependence. This system also enables us to establish a rapid foundation for which to conduct preliminary model refinements.  
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The Monod equation, models the growth of microorganisms in aqueous environments. The underlying assumption is that the growth rates are dependent on the concentration of a limiting nutrient. This equation relates the specific growth rate (µ<sub>g</sub>, the specific growth rate of microorganisms) to the maximum growth rate (µ<sub>m</sub>), the saturation constant (or half-velocity constant, K<sub>s</sub>) as a function of the concentration of the limiting substrate (S) [1]. <br/><br/>
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The three interdependent ''E. coli'' auxotrophs studied in our project are ''ΔtrpA'', ''ΔmetA'', and ''ΔtyrA''. These three strains are deficient in the production of the amino acids tryptophan, methionine, and tyrosine, respectively. Thus, in order for each strain to survive, they must exist in the context of a consortium. Our model is designed to predict the growth and survival of such a system, and is based on the measured amino acid excretion rates under basal genomic expression. The model is written such that it can be easily updated when specific tuning of the amino acid production rates is desired (for example, by introducing induction systems for amino acid production ''in trans''), rendering it ideal for modeling tuned and un-tuned consortium dynamics.  
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<p align=center><img src="https://static.igem.org/mediawiki/2012/b/b1/British_Columbia_2012_MonodEquation.png"> </p>
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The key assumption for this model is that the tryptophan, tethionine, and tyrosine are the only growth limiting substrates for the ''ΔtrpA'', ''ΔmetA'', and ''ΔtyrA'' ''E. coli'' knock outs respectively.
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The three interdependent <i>E. coli</i> auxotrophs used in our project are <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i>. Each of these strains is missing a gene critical in the respective amino acid biosynthetic pathway. Each of these amino acids are treated as the growth limiting substrates. The model is extends on the work recently presented by Kerner and colleagues [2] and attempts to predict the growth and survival of a three-member consortia.<br/><br/>
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One important equation which has been incorporated into our model is the ''Monod equation''. This equation is most commonly used to model microbial growth and has been shown to fit a large variety of empirical data[1]. In addition, it shares the key assumption described above. This equation relates the specific growth rate (µ<sub>g</sub>, the increase in cell mass per unit time) as function of the maximum growth rate (µ<sub>m</sub>), and the concentration of a limiting substrate (S). The equation is written as follows:
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It takes into account a number of factors:<br/>
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<ol>
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<li>The constitutive production of each required amino acid by the two remaining natural prototrophs</li>
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<li>The inducible production of each required amino acid by a recombinant prototroph, and</li>
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<li>The uptake of amino acids in each auxotroph.</li>
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</ol>
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<br/><br/>
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The following sections detail the mathematical foundation of the model, the experimental measurement of the necessary constants, the MATLAB implementation and some preliminary results.<br/><br/>
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[[File:British_Columbia_2012_MonodEquation.png|center|100x200px]]
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<font face=arial narrow size=4><b>Equations and Constants</b></font></br><font face=arial narrow></br>
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where K<sub>S</sub> is the limiting substrate concentration when the specific growth rate is at half maximum (K<sub>S</sub> = S when µ<sub>g</sub> = µ<sub>m</sub>/2). K<sub>S</sub> is also known as the ''saturation constant'' or ''half-velocity constant'''''[1]'''.  
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We proposed the following fundamental equations for our consortium model. In this model, each auxotrophic strain is denoted with a superscript minus (i.e. <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i> are denoted trpA<sup>-</sup>, metA<sup>-</sup>, and tryA<sup>-</sup>, respectively); [Trp], [Met], and [Tyr] represent the concentrations of tryptophan, methionine, and tyrosine; ODtrpA<sup>-</sup>, ODmetA<sup>-</sup>, and ODtyrA<sup>-</sup> represent the optical densities of the <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i> strains; a1, a2, and a3 are the consumption constants with respect to changes in ODtrpA<sup>-</sup>, ODmetA<sup>-</sup>, and ODtyrA<sup>-</sup>, respectively; µtrpA<sup>-</sup>, µmetA<sup>-</sup>, µtyrA<sup>-</sup> are the specific growth rates of the <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i> strains; µMaxtrpA<sup>-</sup>, µMaxmetA<sup>-</sup>, µMaxtyrA<sup>-</sup> are the maximum growth rates of the <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i> strains; and KstrpA<sup>-</sup>, KsmetA<sup>-</sup>, KStyrA<sup>-</sup> are the <i>half-velocity constants</i> of the <i>ΔtrpA</i>, <i>ΔmetA</i>, and <i>ΔtyrA</i> strains. The terms and constants of this equation will be discussed in further detail later.</br></br>
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<p align=center><img src="https://static.igem.org/mediawiki/2012/f/f1/British_Columbia_2012_governing_Equation.png"></p>
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Please note that: equation (7), (8), and (9) can be substituted into equation (4), (5), and (6) directly, and equation (4), (5), (6) after the substitution can be further substituted into equation (1), (2), and (3) to simplify the code further. The overall three equations shown later in the Matlab Code section is based on the described substitution. It is indicated here for viewers' convenience of following the Matlab code later. </br></br>
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==Equations and Constants ==
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In the above equations, some of the key constants for the Monod Kinetics are obtained through analyzing the growth curves in a 96-well plate, with varying amino acid concentrations. Characteristic growth rates were observed and plotted: </br></br>
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Applying the Monod kinetic model, we sought out to measure three variables: the concentration of tryptophan, methionine, and tyrosine in the media available for each specific auxotroph. We were also interested in how these concentrations vary with respect to time.  
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Applying the Monod kinetic model, we sought out to measure three variables: the concentration of tryptophan, methionine, and tyrosine in the media available for each specific auxotroph. We were also interested in how these concentrations vary with respect to time. </br></br>
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As there is very limited initial availability of each amino acid in the media, [https://2012.igem.org/Team:British_Columbia/ProjectConsortia the ability of each auxotroph to grow in coculture] demonstrated that the amino acids necessary for growth are being produced and exported from the cell, and that each auxotroph is feeding on the amino acids produced by the other cells in the consortium. For the sake of the model, we assumed that the environmentally released tryptophan, methionine, and tyrosine were only consumed by the respective auxotrophic strain, and that use of this amino acid was funnelled only into cell growth (i.e. increases in amino acid consumption are attributed to changes in cell growth and not other factors, such as cell maintenance).
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<p align=center><img src="https://static.igem.org/mediawiki/2012/3/3d/GrowthRatesAA_university_of_british_columbia.png"></br><b>Figure 1: Monoculture Growth Rates at various Limiting Amino Acid Concentrations.  
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Combining the above assumptions with Monod kinetics, we proposed the following fundamental equations for our consortium model. In this model, each auxotrophic strain is denoted with a superscript minus (i.e. Δ''trpA'', Δ''metA'', and Δ''tyrA'' are denoted trpA<sup>-</sup>, metA<sup>-</sup>, and tryA<sup>-</sup>, respectively); [Trp], [Met], and [Tyr] represent the concentrations of tryptophan, methionine, and tyrosine; ODtrpA<sup>-</sup>, ODmetA<sup>-</sup>, and ODtyrA<sup>-</sup> represent the optical densities of the Δ''trpA'', Δ''metA'', and Δ''tyrA'' strains; a1, a2, and a3 are the consumption constants with respect to changes in ODtrpA<sup>-</sup>, ODmetA<sup>-</sup>, and ODtyrA<sup>-</sup>, respectively; µtrpA<sup>-</sup>, µmetA<sup>-</sup>, µtyrA<sup>-</sup> are the specific growth rates of the Δ''trpA'', Δ''metA'', and Δ''tyrA'' strains; µMaxtrpA<sup>-</sup>, µMaxmetA<sup>-</sup>, µMaxtyrA<sup>-</sup> are the maximum growth rates of the Δ''trpA'', Δ''metA'', and Δ''tyrA'' strains; and KstrpA<sup>-</sup>, KsmetA<sup>-</sup>, KStyrA<sup>-</sup> are the ''half-velocity constants'' of the Δ''trpA'', Δ''metA'', and Δ''tyrA'' strains.  The terms and constants of this equation will be discussed in further detail later.
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<p align=center></br></br>These data are fit to the Monod equation to determine the constants listed below.</br></br>
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<p align=center><img src="https://static.igem.org/mediawiki/2012/0/05/GrowthRatesAA_LOG_university_of_british_columbia.png"></br>
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Figure 2: Log Scale of Monoculture Growth Rates at various Limiting Amino Acid Concentrations.</b></br></br>
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[[File:British_Columbia_2012_governing_Equation.png|center|500x700px]]
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</br></br>
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Please note that: equation (7), (8), and (9) can be substituted into equation (4), (5), and (6) directly, and equation (4), (5), (6) after the substitution can be further substituted into equation (1), (2), and (3) to simplify the code further. The overall three equations shown later in the [https://2012.igem.org/Team:British_Columbia/Consortia#Matlab_Code Matlab Code] section is based on the described substitution. It is indicated here for viewers' convenience of following the Matlab code later.  
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<p align=left>Note: we used lower amino acid concentrations as well, however we found out that when concentration is below 1E<sup>-7</sup> M, we observed no significant overnight growth of the cells. For a detailed protocol, please visit <a href="https://2012.igem.org/Team:British_Columbia/Protocols/Monod">here</a>.</br></br>
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In the above equations, some of the key constants for the Monod Kinetics are obtained through analyzing the growth curves in a 96-well plate, with varying amino acid concentrations. Characteristic growth rates were observed and plotted:  
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Based on the fits presented in Figures 1 and 2, the key constants, such as maximum growth rates, and half-velocity constants, for each auxotroph were obtained [1]:</br></br><p align=center>
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<img src="https://static.igem.org/mediawiki/2012/b/b8/British_Columbia_2012_constants_from_GrowthRates.png" width=400></p>
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[[file: GrowthRatesAA_university_of_british_columbia.png|center|700px]]
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<p align=left>As discussed in the beginning of this section, we assumed that the amino acid consumption is solely dependent on the cell growth rate. In order to find out the consumption rate constants, (a<sub>1</sub>, a<sub>2</sub>, and a<sub>3</sub>), we need to find out a way to measure the amino acid concentrations in the media with respect to the change of its OD for each auxotroph. To do this, we need to find a way to measure media amino acid concentrations. With no good access to HPLC, we noticed in previous experiments that each of the knockout monocultures will reach to a certain final OD at a different known concentration of amino acid. In addition, recently published paper: ''A Programmable <i>Escherichia coli</i> Consortium via Tunable Symbiosis'', Kerner A et. al (2012) confirmed that there is indeed a relationship between the final auxotroph ODs with the environmental amino acid concentrations [2]. We thus generated a calibration curve to relate the limiting amino acid concentrations to their final OD readings. We based this on our own wet lab data shown in the figures below: </br></br>
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'''Figure 1: Monoculture Growth Rates at various Limiting Amino Acid Concentrations'''
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[[file: GrowthRatesAA_LOG_university_of_british_columbia.png|center| 700px]]
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<p align=center><img src="https://static.igem.org/mediawiki/2012/b/be/ODcalibrationMet_university_of_british_columbia.png"width=600>
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'''Figure 2: Log Scale of Monoculture Growth Rates at various Limiting Amino Acid Concentrations'''
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<p align=left></br><b>Figure 3: Calibration curves used to convert final OD600 to initial amino acid concentration. Standard solutions of known amino acid concentrations were innoculated with the respective auxotroph and grown for 20 hours at 37oC shaken at 200RPM, after which final OD600 were measured. Data were fit to quartic polynomials to inter- polate OD600 readings. Error bars represent standard deviation (n=3).</b></br></br>.
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Note: we used lower amino acid concentrations as well, however we found out that when concentration is below 1E<sup>-7</sup> M, we observed no significant overnight growth of the cells. For a detailed protocol, please visit [https://2012.igem.org/Team:British_Columbia/Protocols/Monod here].
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<p align=left>We then sought out to find out how the amino acid in the environment is consumed with respect to cell growth. </br></br>
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<p align=left>The three auxotrophs were grown in 50 ml flasks with known initial Tryptophan, Methionine, and Tyrosine concentrations (5E<sup>-5</sup> M), respectively, and 5ml of cell free supernatants at various ODs was isolated. These supernatants served as new 96-well plate media for each knock out cultures with newly added M9-Glucose shots (2% glucose in the well) to ensure the amino acids of interest remained as the limiting substrate for all knock outs. A detailed protocol can be found <a href="https://2012.igem.org/Team:British_Columbia/Protocols/AArate">here</a>.  </br></br>
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Based on Figures 1 and 2, the key constants, such as maximum growth rates, and half-velocity constants, for each auxotroph were obtained '''[1]''':
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The cells were grown in a 96-well plate for 15 hours at 37 <sup>o</sup>C, and growth curve data was obtained using a plate reader (most of the monocultures reached stationary phase at this point). </br></br>
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[[File:British_Columbia_2012_constants_from_GrowthRates.png|center|200x300px]]
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Based on the calibration curves (Figure 3), the amino acid concentrations in the wells were calculated from the final OD readings, and when corrected with a dilution factor of 5/4 (as we added in the 5X, it was 1/5 of the total well media volume, as described <a href="https://2012.igem.org/Team:British_Columbia/Protocols/AArate">here</a>), we obtained the resultant amino acid concentrations in the supernatant at various auxotroph monoculture densities. Again, with the assumption that the only change in the amino acid concentration in the media is due to cell growth, and as they are specific knock outs, they were not able to release any amino acid into the environment, each auxotrophs' consumption constants with respect to cell growth rate (a1, a2, a3) can be calculated. Their values are as follows:  </br></br>
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<p align=center><img src="https://static.igem.org/mediawiki/2012/1/1f/ConsumptionRateConstants_university_of_british_columbia.png"width=600></p>
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</br><b>Figure 4. Amino acid consumption measured from auxotroph cultures in minimial media spiked with an initial concentration of 0.15 mM of the required amino acid. Linear regression was used to find the following consumption rates: Trp: 0.304 ± 0.009 mM/OD (a1), Met: 0.115 ± 0.047 mM/OD (a2), Tyr: 0.143 ± 0.020 mM/OD (a3). Error bars represent 95% confidence interval as determined by regression in the calibration curves. </br></br>
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<p align=left>
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Now that we have our consupmtion constants, we were interested in finding out how the other two auxotrophs will contribute to changes in environmental amino acid concentrations.  </br></br>
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As discussed in the beginning of this section, we assumed that the amino acid consumption is solely dependent on the cell growth rate. In order to find out the consumption rate constants, (a1, a2, and a3), we need to find out a way to measure the amino acid concentrations in the media with respect to the change of its OD for each auxotroph. To do this, we need to find a way to measure media amino acid concentrations. With no good access to HPLC, we noticed in previous experiments that each of the knockout monocultures will reach to a certain final OD at a different known concentration of amino acid. In addition, recently published paper: ''A Programmable Escherichia coli Consortium via Tunable Symbiosis'', Kerner A et. al (2012) confirmed that there is indeed a relationship between the final auxotroph ODs with the environmental amino acid concentrations.'''[2]'''. We thus generated a calibration curve to relate the limiting amino acid concentrations to their final OD readings. We based this on our own wet lab data shown in the figures below:  
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With regards to the behaviour of auxotrophs in coculture under changing amino acid concentrations, the closest assumption we can make is that each auxotroph will behave similarly to the conditions of monoculture. Thus, we used the same calibration curve for relating the final OD to the limiting substrate amino acid concentration in the media. The difference here is that, instead of finding out the corresponding limiting amino acid concentration, e.g., <i>ΔmetA</i> media for [Met] measurements, we are interested in measuring the other two non-limiting amino acid concentrations of each knocked out <i>E. coli</i> auxotrophs, e.g. <i>ΔmetA</i> media for [Trp] and [Tyr] concentrations, at various OD readings [2]. We were then able to derive six functions that relate the change of all amino acid concentrations with respect to time (e.g. d[Trp]/dt), based on two auxotrophs' optical densities (e.g. ODmetA<sup>-</sup> and ODtyrA<sup>-</sup>), as shown below: </br></br>
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[[file: ODcalibrationTrp_university_of_british_columbia.png|center| 500x700px]]
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<p align=center><img src="https://static.igem.org/mediawiki/2012/f/f0/ODcalibrationTrp_university_of_british_columbia.png"></br><b>
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'''Figure 3: TrpA Auxotroph Final OD and Trp Concentration Calibration Curve'''
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<p align=center><img src="https://static.igem.org/mediawiki/2012/b/b7/Equation10-15_data_analyzed_data_university_of_british_columbia.png">
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</p>
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Note: These equations can now be directly substituted into equations (1), (2), and (3), in order to simplify the Matlab Code section.</br></br>
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<p align=left>
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One shortcoming in the use of these equations is their dependency on the accuracy of the calibration curves (Figure 3, 4, and 5), as well as the OD readings, especially when OD readings are low and noisy (< 0.1, nearing the limits of the plate reader's accuracy).  </br></br>
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[[file: ODcalibrationMet_university_of_british_columbia.png|center| 500x700px]]
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For example, one of the experimental raw data obtained are shown below: </br></br>
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<p align=center><img src="https://static.igem.org/mediawiki/2012/2/2c/TyrA_in_metAknockoutssupernatants_university_of_british_columbia.png" width=800>
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</br><b>Figure 5: <i>ΔtyrA</i> auxotroph growth curve in various <i>ΔmetA</i> ODs cell free supernatants in 96-well plate reader environment. </b></br></br>.
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'''Figure 4: MetA Auxotrph Final OD and Met Concentration Calibration Curve'''
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<p align=left>
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Note: As discussed before, this may lead to inaccuracies in calculation from low OD readings. All data sets are labelled correspondingly, and triplicate measurements are noted by wells 1, 2, and 3. As we can see, there are some distinct differences in the OD readings among the three different supernatant data sets; however, when corrected by focusing on the difference between the initial reading and the final reading with a base adjustment of 0.025 (the lower value used for all the calibration curves), they average roughly the same, with an average OD of 0.03847, 0.04043, and 0.04218, respectively, for ODmet 0.219, 0.546, and 0.764.  </br></br>
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[[file: ODcalibrationTyr_university_of_british_columbia.png|center| 500x700px]]
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To illustrate this point more clearly, here is a comparison with another set of growth curve data run in the same plate.</br></br>
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<p align=center><img src="https://static.igem.org/mediawiki/2012/0/05/TyrA_and_trpA_in_metAknockoutssupernatants_university_of_british_columbia.png" width=800></br><b>
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Figure 6: Comparison between <i>ΔtrpA</i> and <i>ΔtyrA</i> auxotroph growth curve in the same supernatants of <i>ΔmetA</i> at its various optical density media.</p></b>
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<p align=left>
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Note that all three of the trpA<sup>-</sup> sets of growth curve grew to a much higher OD (reaching about 0.17) at the end of 15 hours and did not reach stationary phase, whereas all tyrA<sup>-</sup> sets grew significantly less, no significant change in OD at the end of the experiment.</br></br>
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'''Figure 5: TyrA Auxotroph Final OD and Tyr Concentration Calibration Curve'''
 
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Note: all the data are the result of triplicate measurements, and a detailed protocol can be found [https://2012.igem.org/Team:British_Columbia/Protocols/AArate here]. We then tested the accuracy of one of the calibration curves. The blue dot in Figure 5 is another data value we obtained later at a known Tyrosine concentration (5E<sup>-5</sup> M), and it is reasonably close to the predicted curve. This test point has proven that this calibration curve can serve us well at this stage of the research; however, we are aware that this curve has limitation, mainly due to the big error bars at the higher OD data points. For future accuracy improvement, more data points (more of the same concentrations as well as more of different concentrations) should be collected.
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<font face=arial narrow size=4><b>Matlab Code</b></font></br><font face=arial narrow></br>
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Secondly, it is necessary to find out how the amino acid in the environment is consumed with respect to cell growth.  
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Now with all the key functions and constants approximated, we are able to encode our model into Matlab, where the six key ODE functions can be solved and output the our prediction of population dynamics. </br></br>
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We grew all three auxotrphs: Δ''trpA'', Δ''metA'', and Δ''tyrA'' in 50 ml flasks with known initial Tryptophan, Methionine, and Tyrosine concentrations (5E-5 M) respectively, and harvested 5ml of cell free supernatants at various ODs. All these supernatants served as new 96-well plate media for each knock out cultures with newly added M9-Glucose shots (2% glucose in the well), which made sure the amino acids of interest remained as the limiting substrate for all knock outs. its detailed protocol can be found [https://2012.igem.org/Team:British_Columbia/Protocols/AArate here].
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The code is shown below, which contains a detailed step by step instruction for future iGEM teams or related scientific researches use, as well as the clarifications of our own code. It is also written is a way that it can be directly copied and pasted into Matlab blank m.files with all the comments in Matlab commenting format already, so that the coder can read the coded instructions and explanations in the coding command window without having to switching between, for example, the matlab and browser windows. </br></br>
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After growing the inoculated 96-well plate in humidified temperature control (37 deg) plate reader for 15 hours, their growth curves had been captured and most of the monocultures had reached their stationary phase, and the plate was then transferred to 37 deg room to obtain the remaining final OD readings.  
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Of course, copying and pasting the code here is welcomed!  </br></br>
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</br></br>
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Code begins here:</br></br>
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% input the following commands, which are all in bold, into MATLAB command window, and not the m.file, before you call the ODE function. </br></br>
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Based on the calibration curves (Figure 3, 4, 5), the amino acid concentrations in the wells were calculated from their final OD readings, and when corrected with a dilution factor of 5/4 (as we added in the 5X, it was 1/5 of the total well media volume, which is described [https://2012.igem.org/Team:British_Columbia/Protocols/AArate here] again), we arrived at the corresponding supernatant amino acid concentrations at various auxotroph monoculture OD readings. Again, with the assumption that the only change in the amino acid concentration in the media is due to cell growths, and as they are specific knock outs, they were not able to produce any amino acid into the environment, each auxotrophs' consumption constants with respect to cell growth rate (a1, 2, 3) can be calculated. Their values are:
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%firstly, define a time array, set the upper limit to be the time (hr) you want this model to predict. For this model to be accurate, it is advised not to use extreme values. </br></br>
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[[file: ConsumptionRateConstants_university_of_british_columbia.png|center| 200x300px]]
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%in our cases, the upper limit is set to 12 as some of the modelling equations are based on 11 hour culture supernatant data. Thus accuracy is expected within 12 hours. </br></br>
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% xspan = [0 12]; </br></br>
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Thirdly, after founding out the consumption constants for corresponding auxotrophs, we were interested in finding out how the other two auxotrophs will contribute to this specific amino acid changes in the environment. After all, these equations (f(ODtrpA-)s, f(ODmetA-)s, and f(ODtyrA-)s in equation (1), (2), and (3)) are the key to the accuracy of this model.
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%in order to set up the initial conditions, where they corespond to the initial values of y(1), y(2), y(3)..., to y(6) in our case, and y(n) if you have n number of ODE equations </br></br>
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In the coculture environment, for the behaviour of auxotrophs with respect to changing the environment amino acid concentrations, the closest assumption we can make is that teach auxotroph will behave similarly to its behaviour in its monoculture condition. Thus, we used the same calibration curve for relating the final OD to the limiting substrate amino acid concentration in the media. The difference is that in stead of finding out the corresponding limiting amino acid concentration, e.g., Δ''metA'' media for [Met] measurements, this time, we are finding out the other two non-limiting amino acid concentrations of each knocked out ''E. coli'' auxotrophs, e.g. Δ''metA'' media for [Trp] and {Tyr] concentrations, at various OD readings '''[2]'''. With the consideration of sample taken time and some data manipulation, we were able to derive six functions that relate the change of all amino acid concentrations with respect to time (e.g. d[Trp]/dt), with the other two auxotrophs' optical densities (e.g. ODmetA- and ODtyrA-), which is shown below:
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% ynot = [4E-5 4E-5 4E-5 0.05 0.05 0.05]'; </br></br>
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[[file: equation10-15_data_analyzed_data_university_of_british_columbia.png|center| 700x900px]]
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% the first three values are the initial values for y(1), y(2), and y(3) respectively, which in out cases are the initial amino acid concentrations, and as we are adding the cells directly into the M9 media without supplementing any amino acids, it is believed to be at a low concentrations, though there will be some left over amino acids within the living initial cells, and thus is set to 4E-5 mole/L. </br></br>
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Note: all these equations have been previously mentioned in equation (1), (2), and (3), and can be directly substituted into them later to reduce the number of equations in the [https://2012.igem.org/Team:British_Columbia/Consortia#Matlab_Code Matlab Code] section.
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% the remaining three values are the initial conditions for y(4), y(5), and y(6), which are the initial cell ODs in out case, and they are all set to 0.05, which is the initial OD reading we obtained after we innoculated the culture, with the assumption that all the cells are still alive. </br></br>
-
One big '''limitation''' of the accuracy of these equations is that their accuracy is extremely dependent on the accuracy of the calibration curves (Figure 3, 4, and 5) as well as precision and accuracy of the OD readers, especially when OD readings are low. As you can see from Figure 3, 4, and 5, the slope is extremely high for all of them at low OD values, and thus are very sensitive to inaccurate low OD readings (OD<0.1).
+
%Then RUN THE FUNCTION </br></br>
-
However, some of the equations are derived from the available data assuming that at low OD values, it is still reasonable to use the calibration curve to calculate the supernatant (cell free media) amino acid concentration.
+
% [X,Y] = ode45(@UBCiGEM2012_ConsortiaModel,xspan,ynot); </br></br>
-
Especially for Equation (15), its raw experimental OD readings are shown below:
+
% this takes the function name in your m.file, the range of your interest, and the initial conditions which is just defined, respectively </br></br>
-
[[file: tyrA_in_metAknockoutssupernatants_university_of_british_columbia.png|center| 900x1000px]]
+
%Note: have to make sure that the name is exactly the same as the function name in your m.file for it to be able to run correctly. </br></br>
-
'''Figure 6:  Δ''tyrA'' auxotroph growth curve in various Δ''metA'' ODs cell free supernatants in 96-well plate reader environment
+
%the following code is for the actual mfile. </br></br>
-
Note: As discussed before, all of this data can cause inaccurate supernatant concentration calculation due to its extremely low OD readings. All data sets are labelled correspondingly, where triplicates are noted by well 1, 2, and 3. As we can see, there are some distinct differences in the OD readings among the three different supernatant data sets; however, when corrected through focusing on the difference between the initial reading and the final reading with a base adjustment of 0.025 (which is the lower value used for all the calibration curves), they average roughly the same, with an average OD value of 0.03847, 0.04043, and 0.04218, respectively, for ODmet 0.219, 0.546, and 0.764. To make sure you understand this calculation fully, Here is a sample calculation: for example, suppose one set of the data has an initial OD reading of 0.02, and a final OD reading of 0.04, then we conclude that there is a difference/growth of (0.04-0.03)=0.01 OD of cells, and then it is corrected with the base OD reading, 0.025, for calibration curve applicability. Thus, it reaches a effective final OD of (0.025+0.01)=0.035. This process is replicated for all triplicates, and by averaging these three effective final ODs, the average OD value is used for concentration determination is arrived.
+
% 1) Declare the name of the function: </br></br>
 +
function dy = UBCiGEM2012_ConsortiaModel(t,y); </br></br>
-
To demonstrate more clearly that this function (equation (15)) can be an outlier, here is a comparison with one other sets of growth curve that is run in the same plate by plotting them together.  
+
% 2) define all your ODE variables clearly for your own understanding as they can get quite confusing if you don't. </br></br>
-
[[file: tyrA_and_trpA_in_metAknockoutssupernatants_university_of_british_columbia.png|center| 900x1000px]]
+
% in our case: </br></br>
-
'''Figure 7:  Δ''tyrA'' auxotroph growth curve in various Δ''metA'' ODs cell free supernatants in 96-well plate reader environment
+
% y(1) = [Trp]; </br></br>
-
Note: this set of data is not just any random data set. As you can see from the legend, both of the data sets are obtained as auxotrophs ( Δ''tyrA'' and Δ''trpA'') growing in the '''same supernatants''' that were obtained from Δ''metA'' growing media at various ODs. Also, to make it more obvious for readers to get a sense of raw data, it is '''shape labelled''' as triangles, circles, and squares for OD 0.219, 0.546 and 0.764 respectively.
+
% dy(1) = d[Trp]/dt; </br></br>
-
It is easy to notice that all three of the trpA- sets of growth curve grew to a much higher OD (reaching about 0.17) at the end of 15 hours and was still growing, whereas all tyrA- sets grew significantly less, with an average initial and final OD differences of only about 0.01.
+
% y(2) = [Met]; </br></br>
-
==Matlab Code==
+
% dy(2) = d[Met]/dt; </br></br>
-
Now with all the key functions and constants approximated, we are able to encode our model into Matlab, where the six key ODE functions can be solved and output the our prediction of population dynamics
+
% y(3) = [Tyr]; </br></br>
-
The code is shown below, which contains a '''detailed step by step instruction''' for '''future iGEM teams or related scientific researches use''', as well as the clarifications of our own code. It is also written is a way that it can be directly copied and pasted into Matlab blank m.files with all the comments in Matlab commenting format already, so that the coder can read the coded instructions and explanations in the coding command window without having to switching between, for example, the matlab and browser windows.
+
% dy(3) = d[Tyr]/dt; </br></br>
-
Of course, copying and pasting the code here is welcomed!
+
% y(4) = [OD_trpA-]; </br></br>
-
'''Code begins here:'''
+
% dy(4) = d[OD_trpA-]/dt; </br></br>
-
% input the following commands, which are all in '''bold''', into MATLAB command window, and '''not the m.file''', before you call the ODE function. 
+
% y(5) = [OD_tyrA-]; </br></br>
-
%firstly, define a time array, set the upper limit to be the time (hr) you want this model to predict. For this model to be accurate, it is advised not to use extreme values.
+
% dy(5) = d[OD_tyrA-]/dt; </br></br>
-
%in our cases, the upper limit is set to 12 as some of the modelling equations are based on 11 hour culture supernatant data. Thus accuracy is expected within 12 hours.
+
% y(6) = [OD_metA-]; </br></br>
-
%''' >> xspan = [0 12]''';
+
% dy(6) = d[OD_metA-]/dt; </br></br>
-
%in order to set up the initial conditions, where they corespond to the initial values of y(1), y(2), y(3)..., to y(6)  in our case, and y(n) if you have ''n'' number of ODE equations
+
% 3) define your constants first that will not depend on variables </br></br>
-
% >>''' ynot = [1E-7 1E-7 1E-7 0.01 0.01 0.01]';'''
+
% in our case, it was all measured through our own wet lab data and is explained in the previous section </br></br>
-
% the first three values are the initial values for y(1), y(2), and y(3) respectively, which in out cases are the initial amino acid concentrations, and as we are adding the cells directly into the M9 media without supplementing any amino acids, it is believed to be at very low concentrations, and thus is set to 1E-7 mole/L.
+
%define maximum growth rate constants </br></br>
-
% the remaining three values are the initial conditions for y(4), y(5), and y(6), which are the initial cell ODs in out case, and they are all set to 0.01, which is based on the lower values of the OD plate readings after correcting with blanks.
+
u_maxtrpA = 0.23; % units = hr-1 </br></br>
-
%Then RUN THE FUNCTION
+
u_maxmetA = 0.34; % units = hr-1 </br></br>
-
% >>'''[X,Y] = ode45(@UBCiGEM2012_ConsortiaModel,xspan,ynot);'''
+
u_maxtyrA = 0.28; % units = hr-1 </br></br>
-
% this takes the function name in your m.file, the range of your interest, and the initial conditions which is just defined, respectively
+
%define half-velocity constants </br></br>
-
%Note: have to make sure that the name is exactly the same as the function name in your m.file for it to be able to run correctly.
+
Ks_trpA = 3.30E-7; % units = M </br></br>
 +
Ks_metA = 5.10E-6; % units = M </br></br>
-
'''%the following code is for the actual mfile.'''
+
Ks_tyrA = 8.50E-7; % units = M </br></br>
-
% 1) Declare the name of the function:
+
%define the consumption rate constants </br></br>
-
function dy = UBCiGEM2012_ConsortiaModel(t,y);
+
a1 = 3.05E-4; %units= M/OD trp</br></br>
-
% 2) define all your ODE variables clearly for your own understanding as they can get quite confusing if you don't.  
+
a2 = 1.15E-4; %units= M/OD met</br></br>
-
% in our case:
+
a3 = 1.4E-4; %units= M/OD tyr</br></br>
-
'''% y(1) = [Trp];
+
% 4) write out ODE CODE, suggest to write out all equations on paper first, as they are a lot more clear than coding them directly, and will reduce the chance for you to make error why coding significantly. </br></br>
-
'''% dy(1) = d[Trp]/dt
+
dy = zeros(6,1); </br></br>
-
'''% y(2) = [Met];
+
dy(1) = 2.5E-7 + 7.2E-8 - a1*(u_maxtrpA*(y(1))/(Ks_trpA+y(1)))*y(4); </br></br>
-
'''% dy(2) = d[Met]/dt;
+
dy(2) = 1.7E-7 + 5.1E-8 - a2*(u_maxmetA*(y(2))/(Ks_metA+y(2)))*y(5); </br></br>
-
'''% y(3) = [Tyr];
+
dy(3) = -3.6E-8 + 1.7E-6 - a3*(u_maxtyrA*(y(3))/(Ks_tyrA+y(3)))*y(6); </br></br>
-
'''% dy(3) = d[Tyr]/dt;
+
dy(4) = (u_maxtrpA*y(1))/(Ks_trpA+y(1))*(y(4)); </br></br>
-
'''% y(4) = [OD_trpA-];
+
dy(5) = (u_maxmetA*y(2))/(Ks_metA+y(2))*(y(5)); </br></br>
-
'''% dy(4) = d[OD_trpA-]/dt;
+
dy(6) = (u_maxtyrA*y(3))/(Ks_tyrA+y(3))*(y(6)); </br></br>
-
'''% y(5) = [OD_tyrA-];
+
% note: For easy coding and checking purposes, all this equations are exactly the same as the one shown in equation (1), (2), and (3) for dy(1), dy(2), and dy(3) respectively, with also the same arrangements of each equation term within them. </br></br>
-
'''% dy(5) = d[OD_tyrA-]/dt;
+
% and Please also note: these equation has been derived with direct simple substitution of equations, where equation (4), (5), (6), (7), (8), (9), (10), (11), (12), (13), (14) and (15) have been substituted into the equation (1), (2) and (3). </br></br>
-
'''% y(6)  = [OD_metA-];
+
%if you want, you can choose to plot diagrams and display the predicting dynamics within the m.file or manually plot the arrays of your interests in command window </br></br>
-
'''% dy(6) = d[OD_metA-]/dt;'''
+
%For example, for this project, we are interesting in predicting the ODs of each auxotrophs, thus, we manually plotted values in y(4), y(5), and y(6) (which again, are the OD values of each auxotrophs) agaisnt time, by typing the following in the command window after run the ODE45 functions: </br></br>
 +
%>>plot(X,Y(:,4:6)) </br></br>
-
% 3) define your constants first that will not depend on variables
+
</br></br>
 +
End of the m.file </br></br>
-
% in our case, it was all measured through our own wet lab data and is explained in the previous section
 
-
%define maximum growth rate constants
+
<font face=arial narrow size=4><b>Simulation Results, Analysis and Future Work</b></font></br><font face=arial narrow>
 +
</br>
 +
When the Matlab Code was run, it generated a graphic result predicting the population dynamics without inducing, shown below: </br></br>
-
u_maxtrpA = 0.2442;      % units = hr-1
+
<p align=center><img src="https://static.igem.org/mediawiki/2012/c/c8/12hours_nomordificationprediction_university_of_british_columbia.png" width=700></br>
-
u_maxmetA = 0.5524;    % units = hr-1
+
<b>Figure 7. A comparison of model predictions versus empirical measurements (n=3) of each auxotroph’s growth in a co-culture.</br></br>
-
u_maxtyrA = 0.3332;      % units = hr-1
+
<p align=left>As shown in the figure above, this model can give reasonable predictions though still can be further modified for better accuracy. For  our <b>future work</b>, we will refine our model with more experimental data. This can be accomplished by running the <a href="https://2012.igem.org/Team:British_Columbia/Protocols/AArate">amino acid</a> experiment data points of interest. Alternatively, we can use more direct methods of amino acid measurement, like HPLC analysis.</br></br>
 +
This code can also be adapted to predict the population dynamics of our cocultures when tuning effects are introduced. This is accomplished by by generating six new functions to describe the amino acid dynamics of each monoculture. We can then use this model to decide the amount of tuning (by adding in different amount of inducer) needed to obtain a desired population of each auxotroph.</br></br>
-
%define half-velocity constants
+
<font face=arial narrow size=4><b>References</b></font></br><font face=arial narrow></br>
-
Ks_trpA = 1E-7;       % units = M
+
[1] Shuler, ML; Kargi, F; Bioprocess Engineering Basic Concepts, 2002 2nd edition. Prentice Hall PTR.</br></br>
-
Ks_metA = 5E-6;      % units = M
+
[2] Kerner A; Park J; Williams A; Lin XN; A Programmable Escherichia coli Consortium via Tunable Symbiosis, 2012. PLoS ONE 7(3): e34032. doi:10.1371/journal.pone.0034032
-
 
+
-
Ks_tyrA = 8.36E-8;  % units = M
+
-
 
+
-
 
+
-
%define the consumption rate constants
+
-
 
+
-
a1 = 4.08E-4;  %units= M/OD
+
-
 
+
-
a2 = 1.44E-4;  %units= M/OD
+
-
 
+
-
a3 = 1.23E-5;  %units= M/OD
+
-
 
+
-
 
+
-
% 4) write out ODE CODE, suggest to write out all equations on paper first, as they are a lot more clear than coding them directly, and will reduce the chance for you to make error why coding significantly.
+
-
 
+
-
dy = zeros(6,1);
+
-
 
+
-
dy(1) = ((1e-8)*(exp(1))^(16.586*y(4)))  +        ((-0.023*(y(5))^2)+0.021*y(5)-3.5e-3) - a1*(u_maxtrpA*(y(1))/(Ks_trpA+y(1)))*y(4);
+
-
 
+
-
dy(2) = ((9e-6)*(y(6))^2-(7e-6)*y(6)+1e-6)  +  (((2e-6)*(y(4))^2)-(2e-6)*y(4)+9e-7)    - a2*(u_maxmetA*(y(2))/(Ks_metA+y(2)))*y(5);
+
-
 
+
-
dy(3) = ((3e-7)*(y(4))^2-(8e-7)*y(4)+4e-7)  +  (((1e-6)*(y(5))^2)-(2e-6)*y(5)+6e-7)    - a3*(u_maxtyrA*(y(3))/(Ks_tyrA+y(3)))*y(6);
+
-
 
+
-
dy(4) = (u_maxtrpA*y(1))/(Ks_trpA+y(1))*(y(4));
+
-
 
+
-
dy(5) = (u_maxmetA*y(2))/(Ks_metA+y(2))*(y(5));
+
-
 
+
-
dy(6) = (u_maxtyrA*y(3))/(Ks_tyrA+y(3))*(y(6));
+
-
 
+
-
% note: '''For easy coding and checking purposes, all this equations are exactly the same as the one shown in equation (1), (2), and (3) for dy(1), dy(2), and dy(3) respectively, with also the same arrangements of each equation term within them.'''
+
-
 
+
-
% and '''Please also note:''' these equation has been derived with direct simple substitution of equations, where equation (4), (5), (6), (7), (8), (9), (10), (11), (12), (13), (14) and (15) have been substituted into the equation (1), (2) and (3).
+
-
 
+
-
%if you want, you can choose to plot diagrams and display the predicting dynamics within the m.file or manually plot the arrays of your interests in command window
+
-
 
+
-
%For example, for this project, we are interesting in predicting the ODs of each auxotrophs, thus, we manually plotted values in y(4), y(5), and y(6) (which again, are the OD values of each auxotrophs) agaisnt time, by typing the following in the command window after run the ODE45 functions:
+
-
 
+
-
'''%>>plot(X,Y(:,4:6)) '''
+
-
 
+
-
 
+
-
'''End of the m.file'''
+
-
 
+
-
 
+
-
'''Note: I will indicate here that the direct translation of the previous discussed equations with simple substitutions at either the beginning or the end of this section'''
+
-
 
+
-
==Simulation Results, Analysis and Future Work==
+
-
 
+
-
When the [https://2012.igem.org/Team:British_Columbia/Consortia#Matlab_Code Matlab Code] was run, it generated a graphic result predicting the population dynamics without inducing, which is shown in the following diagram:
+
-
 
+
-
 
+
-
[[file: 12hours_nomordificationprediction_university_of_british_columbia.png|Center| 700x700px]]
+
-
 
+
-
'''Figure 8. Population Dynamics Prediction within 12-hour Confidence Range'''
+
-
 
+
-
 
+
-
[[file: 16hours_nomordificationprediction_university_of_british_columbia.png|Center| 700x700px]]
+
-
 
+
-
'''Figure 9. Population Dynamics Prediction for 16 hours'''
+
-
+
-
As we can see here, the prediction is no longer reasonable as the Δ''tyrA'' OD shoot off into 1.8 after 16 hours of incubation.
+
-
 
+
-
'''However''', as we explained in the end of [https://2012.igem.org/Team:British_Columbia/Consortia#Equations_and_Constants Equation and Constants] section, equation (15) can be an outlier equation for this modelling, and the [Tyr] concentration is proven to be so low in the environment that we can barely observe any growth even with the precisions of an high-end plate reader.
+
-
 
+
-
Thus, we were very interested in finding out how the predictions would be differed if we took out equation (15) from the simulation code, as if we assumed the environment tyrosine concentration had no relationship with Δ''metA'' growth. The result of both 12 and 16 hour predictions are shown in Figure 10, and 11.
+
-
 
+
-
[[file: 12hours_Withmordificationpredictionno32_university_of_british_columbia.png|Center| 700x700px]]
+
-
 
+
-
'''Figure 10. Population Dynamics 12-hour Prediction after eliminating one potential inaccurate function, equation (15)'''
+
-
 
+
-
[[file: 16hours_Withmordificationpredictionno32_university_of_british_columbia.png|Center| 700x700px]]
+
-
 
+
-
'''Figure 11. Population Dynamics 16-hour Prediction after eliminating one potential inaccurate function, equation (15)'''
+
-
 
+
-
From these figures, it was not surprising to see that the prediction become reasonable again for 16 hours one too, as the sum of the ODs sum ups to about 1, which proved that the attempt of generating the functions to relate d[Tyr]/dt as a function of ODmetA- based on extremely small OD reading values, had more negative effect on the accuracy of the modelling than assuming that the growth related tyrosine production or consumption of the environment was so insignificant and could thus be assumed to be zero (d[Tyr]/dt= f(ODmetA-)tyr=0).
+
-
 
+
-
Also, please note that the 12 hour prediction with this equation elimination (Figure 11) is almost identical to the prediction when equation (15) is still in the code, where the effect of equation (15) can be better visualized in Figure 12. This also confirmed that when the time was with 12 hours, this modelling could work reasonably well, even if some of the equations used was not accurate, it was empirically accurate within our confidence range at least.
+
-
 
+
-
[[file: 12hours_comparisionWithwithoutmordificationpredictionno32_university_of_british_columbia.png|Center| 700x700px]]
+
-
 
+
-
'''Figure 12. Population Dynamics 12-hour predictions with and without eliminating one potential inaccurate function, equation (15)'''
+
-
 
+
-
Thus, for '''future work''', now that we have demonstrated that our model can work, and we can make it even better if we have more qualitative/accurate data, or more quantitive data (more data to generate more accurate calibration or function curves) in general. It can be done through running the [https://2012.igem.org/Team:British_Columbia/Protocols/AArate generation of amino acid] experiment with more of OD of interest, or, instead of using the final OD calibration curves, we can try with another more accurate and direct measurement of the supernatant amnio acid concentrations, such as HPLC.
+
-
 
+
-
Also, we can set up experiments to test the accuracy of the population dynamics through taking samples at various time of the coculture flasks and scan the sample using fluorscence reader to get the real population dynamics in the actual consortia, and compare it with this modelling prediction (now our wet lab consortium population dynamic data are solely based on 96-well plate environment).
+
-
 
+
-
Lastly, as mentioned at the description, this code can be generalized to predict the population dynamics when there is a tuning effect, as long as we can generate six new functions describing the amino acid dynamics in each monoculture. Therefore, we can use this model to decide the amount of tuning (by adding in different amount of inducer) needed to obtain the desired relative population of each auxotrophs.
+
-
 
+
-
==References==
+
-
 
+
-
'''[1]''' Shuler, ML; Kargi, F; Bioprocess Engineering Basic Concepts, 2002 2nd edition. Prentice Hall PTR.
+
-
 
+
-
'''[2]'''      Kerner A; Park J; Williams A; Lin XN; A Programmable Escherichia coli Consortium via Tunable Symbiosis, 2012. PLoS ONE 7(3): e34032. doi:10.1371/journal.pone.0034032
+

Latest revision as of 04:06, 26 October 2012

British Columbia - 2012.igem.org

Modeling Microbial Consortia: The Auxotroph Approach

There is a large potential combinatorial space for optimizing and tuning a three-member microbial consortium with auxotrophic interdependence. In order to help address this concern, we set to develop a computational framework to help guide experimental decisions. Our model attempts to develop a predictive understanding of auxotrophic interdependence in a three-member microbial consortium. As it was important for us to be able to predict growth kinetics based on measurable, empirical properties, as well as to develop a mathematically and experimentally tractable system, we decided to base our model on Monod kinetics.

The Monod equation, models the growth of microorganisms in aqueous environments. The underlying assumption is that the growth rates are dependent on the concentration of a limiting nutrient. This equation relates the specific growth rate (µg, the specific growth rate of microorganisms) to the maximum growth rate (µm), the saturation constant (or half-velocity constant, Ks) as a function of the concentration of the limiting substrate (S) [1].

The three interdependent E. coli auxotrophs used in our project are ΔtrpA, ΔmetA, and ΔtyrA. Each of these strains is missing a gene critical in the respective amino acid biosynthetic pathway. Each of these amino acids are treated as the growth limiting substrates. The model is extends on the work recently presented by Kerner and colleagues [2] and attempts to predict the growth and survival of a three-member consortia.

It takes into account a number of factors:
  1. The constitutive production of each required amino acid by the two remaining natural prototrophs
  2. The inducible production of each required amino acid by a recombinant prototroph, and
  3. The uptake of amino acids in each auxotroph.


The following sections detail the mathematical foundation of the model, the experimental measurement of the necessary constants, the MATLAB implementation and some preliminary results.

Equations and Constants

We proposed the following fundamental equations for our consortium model. In this model, each auxotrophic strain is denoted with a superscript minus (i.e. ΔtrpA, ΔmetA, and ΔtyrA are denoted trpA-, metA-, and tryA-, respectively); [Trp], [Met], and [Tyr] represent the concentrations of tryptophan, methionine, and tyrosine; ODtrpA-, ODmetA-, and ODtyrA- represent the optical densities of the ΔtrpA, ΔmetA, and ΔtyrA strains; a1, a2, and a3 are the consumption constants with respect to changes in ODtrpA-, ODmetA-, and ODtyrA-, respectively; µtrpA-, µmetA-, µtyrA- are the specific growth rates of the ΔtrpA, ΔmetA, and ΔtyrA strains; µMaxtrpA-, µMaxmetA-, µMaxtyrA- are the maximum growth rates of the ΔtrpA, ΔmetA, and ΔtyrA strains; and KstrpA-, KsmetA-, KStyrA- are the half-velocity constants of the ΔtrpA, ΔmetA, and ΔtyrA strains. The terms and constants of this equation will be discussed in further detail later.

Please note that: equation (7), (8), and (9) can be substituted into equation (4), (5), and (6) directly, and equation (4), (5), (6) after the substitution can be further substituted into equation (1), (2), and (3) to simplify the code further. The overall three equations shown later in the Matlab Code section is based on the described substitution. It is indicated here for viewers' convenience of following the Matlab code later.

In the above equations, some of the key constants for the Monod Kinetics are obtained through analyzing the growth curves in a 96-well plate, with varying amino acid concentrations. Characteristic growth rates were observed and plotted:

Applying the Monod kinetic model, we sought out to measure three variables: the concentration of tryptophan, methionine, and tyrosine in the media available for each specific auxotroph. We were also interested in how these concentrations vary with respect to time.


Figure 1: Monoculture Growth Rates at various Limiting Amino Acid Concentrations.



These data are fit to the Monod equation to determine the constants listed below.


Figure 2: Log Scale of Monoculture Growth Rates at various Limiting Amino Acid Concentrations.




Note: we used lower amino acid concentrations as well, however we found out that when concentration is below 1E-7 M, we observed no significant overnight growth of the cells. For a detailed protocol, please visit here.

Based on the fits presented in Figures 1 and 2, the key constants, such as maximum growth rates, and half-velocity constants, for each auxotroph were obtained [1]:

As discussed in the beginning of this section, we assumed that the amino acid consumption is solely dependent on the cell growth rate. In order to find out the consumption rate constants, (a1, a2, and a3), we need to find out a way to measure the amino acid concentrations in the media with respect to the change of its OD for each auxotroph. To do this, we need to find a way to measure media amino acid concentrations. With no good access to HPLC, we noticed in previous experiments that each of the knockout monocultures will reach to a certain final OD at a different known concentration of amino acid. In addition, recently published paper: ''A Programmable Escherichia coli Consortium via Tunable Symbiosis'', Kerner A et. al (2012) confirmed that there is indeed a relationship between the final auxotroph ODs with the environmental amino acid concentrations [2]. We thus generated a calibration curve to relate the limiting amino acid concentrations to their final OD readings. We based this on our own wet lab data shown in the figures below:


Figure 3: Calibration curves used to convert final OD600 to initial amino acid concentration. Standard solutions of known amino acid concentrations were innoculated with the respective auxotroph and grown for 20 hours at 37oC shaken at 200RPM, after which final OD600 were measured. Data were fit to quartic polynomials to inter- polate OD600 readings. Error bars represent standard deviation (n=3).

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We then sought out to find out how the amino acid in the environment is consumed with respect to cell growth.

The three auxotrophs were grown in 50 ml flasks with known initial Tryptophan, Methionine, and Tyrosine concentrations (5E-5 M), respectively, and 5ml of cell free supernatants at various ODs was isolated. These supernatants served as new 96-well plate media for each knock out cultures with newly added M9-Glucose shots (2% glucose in the well) to ensure the amino acids of interest remained as the limiting substrate for all knock outs. A detailed protocol can be found here.

The cells were grown in a 96-well plate for 15 hours at 37 oC, and growth curve data was obtained using a plate reader (most of the monocultures reached stationary phase at this point).

Based on the calibration curves (Figure 3), the amino acid concentrations in the wells were calculated from the final OD readings, and when corrected with a dilution factor of 5/4 (as we added in the 5X, it was 1/5 of the total well media volume, as described here), we obtained the resultant amino acid concentrations in the supernatant at various auxotroph monoculture densities. Again, with the assumption that the only change in the amino acid concentration in the media is due to cell growth, and as they are specific knock outs, they were not able to release any amino acid into the environment, each auxotrophs' consumption constants with respect to cell growth rate (a1, a2, a3) can be calculated. Their values are as follows:


Figure 4. Amino acid consumption measured from auxotroph cultures in minimial media spiked with an initial concentration of 0.15 mM of the required amino acid. Linear regression was used to find the following consumption rates: Trp: 0.304 ± 0.009 mM/OD (a1), Met: 0.115 ± 0.047 mM/OD (a2), Tyr: 0.143 ± 0.020 mM/OD (a3). Error bars represent 95% confidence interval as determined by regression in the calibration curves.

Now that we have our consupmtion constants, we were interested in finding out how the other two auxotrophs will contribute to changes in environmental amino acid concentrations.

With regards to the behaviour of auxotrophs in coculture under changing amino acid concentrations, the closest assumption we can make is that each auxotroph will behave similarly to the conditions of monoculture. Thus, we used the same calibration curve for relating the final OD to the limiting substrate amino acid concentration in the media. The difference here is that, instead of finding out the corresponding limiting amino acid concentration, e.g., ΔmetA media for [Met] measurements, we are interested in measuring the other two non-limiting amino acid concentrations of each knocked out E. coli auxotrophs, e.g. ΔmetA media for [Trp] and [Tyr] concentrations, at various OD readings [2]. We were then able to derive six functions that relate the change of all amino acid concentrations with respect to time (e.g. d[Trp]/dt), based on two auxotrophs' optical densities (e.g. ODmetA- and ODtyrA-), as shown below:


Note: These equations can now be directly substituted into equations (1), (2), and (3), in order to simplify the Matlab Code section.

One shortcoming in the use of these equations is their dependency on the accuracy of the calibration curves (Figure 3, 4, and 5), as well as the OD readings, especially when OD readings are low and noisy (< 0.1, nearing the limits of the plate reader's accuracy).

For example, one of the experimental raw data obtained are shown below:


Figure 5: ΔtyrA auxotroph growth curve in various ΔmetA ODs cell free supernatants in 96-well plate reader environment.

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Note: As discussed before, this may lead to inaccuracies in calculation from low OD readings. All data sets are labelled correspondingly, and triplicate measurements are noted by wells 1, 2, and 3. As we can see, there are some distinct differences in the OD readings among the three different supernatant data sets; however, when corrected by focusing on the difference between the initial reading and the final reading with a base adjustment of 0.025 (the lower value used for all the calibration curves), they average roughly the same, with an average OD of 0.03847, 0.04043, and 0.04218, respectively, for ODmet 0.219, 0.546, and 0.764.

To illustrate this point more clearly, here is a comparison with another set of growth curve data run in the same plate.


Figure 6: Comparison between ΔtrpA and ΔtyrA auxotroph growth curve in the same supernatants of ΔmetA at its various optical density media.

Note that all three of the trpA- sets of growth curve grew to a much higher OD (reaching about 0.17) at the end of 15 hours and did not reach stationary phase, whereas all tyrA- sets grew significantly less, no significant change in OD at the end of the experiment.

Matlab Code

Now with all the key functions and constants approximated, we are able to encode our model into Matlab, where the six key ODE functions can be solved and output the our prediction of population dynamics.

The code is shown below, which contains a detailed step by step instruction for future iGEM teams or related scientific researches use, as well as the clarifications of our own code. It is also written is a way that it can be directly copied and pasted into Matlab blank m.files with all the comments in Matlab commenting format already, so that the coder can read the coded instructions and explanations in the coding command window without having to switching between, for example, the matlab and browser windows.

Of course, copying and pasting the code here is welcomed!



Code begins here:

% input the following commands, which are all in bold, into MATLAB command window, and not the m.file, before you call the ODE function.

%firstly, define a time array, set the upper limit to be the time (hr) you want this model to predict. For this model to be accurate, it is advised not to use extreme values.

%in our cases, the upper limit is set to 12 as some of the modelling equations are based on 11 hour culture supernatant data. Thus accuracy is expected within 12 hours.

% xspan = [0 12];

%in order to set up the initial conditions, where they corespond to the initial values of y(1), y(2), y(3)..., to y(6) in our case, and y(n) if you have n number of ODE equations

% ynot = [4E-5 4E-5 4E-5 0.05 0.05 0.05]';

% the first three values are the initial values for y(1), y(2), and y(3) respectively, which in out cases are the initial amino acid concentrations, and as we are adding the cells directly into the M9 media without supplementing any amino acids, it is believed to be at a low concentrations, though there will be some left over amino acids within the living initial cells, and thus is set to 4E-5 mole/L.

% the remaining three values are the initial conditions for y(4), y(5), and y(6), which are the initial cell ODs in out case, and they are all set to 0.05, which is the initial OD reading we obtained after we innoculated the culture, with the assumption that all the cells are still alive.

%Then RUN THE FUNCTION

% [X,Y] = ode45(@UBCiGEM2012_ConsortiaModel,xspan,ynot);

% this takes the function name in your m.file, the range of your interest, and the initial conditions which is just defined, respectively

%Note: have to make sure that the name is exactly the same as the function name in your m.file for it to be able to run correctly.

%the following code is for the actual mfile.

% 1) Declare the name of the function:

function dy = UBCiGEM2012_ConsortiaModel(t,y);

% 2) define all your ODE variables clearly for your own understanding as they can get quite confusing if you don't.

% in our case:

% y(1) = [Trp];

% dy(1) = d[Trp]/dt;

% y(2) = [Met];

% dy(2) = d[Met]/dt;

% y(3) = [Tyr];

% dy(3) = d[Tyr]/dt;

% y(4) = [OD_trpA-];

% dy(4) = d[OD_trpA-]/dt;

% y(5) = [OD_tyrA-];

% dy(5) = d[OD_tyrA-]/dt;

% y(6) = [OD_metA-];

% dy(6) = d[OD_metA-]/dt;

% 3) define your constants first that will not depend on variables

% in our case, it was all measured through our own wet lab data and is explained in the previous section

%define maximum growth rate constants

u_maxtrpA = 0.23; % units = hr-1

u_maxmetA = 0.34; % units = hr-1

u_maxtyrA = 0.28; % units = hr-1

%define half-velocity constants

Ks_trpA = 3.30E-7; % units = M

Ks_metA = 5.10E-6; % units = M

Ks_tyrA = 8.50E-7; % units = M

%define the consumption rate constants

a1 = 3.05E-4; %units= M/OD trp

a2 = 1.15E-4; %units= M/OD met

a3 = 1.4E-4; %units= M/OD tyr

% 4) write out ODE CODE, suggest to write out all equations on paper first, as they are a lot more clear than coding them directly, and will reduce the chance for you to make error why coding significantly.

dy = zeros(6,1);

dy(1) = 2.5E-7 + 7.2E-8 - a1*(u_maxtrpA*(y(1))/(Ks_trpA+y(1)))*y(4);

dy(2) = 1.7E-7 + 5.1E-8 - a2*(u_maxmetA*(y(2))/(Ks_metA+y(2)))*y(5);

dy(3) = -3.6E-8 + 1.7E-6 - a3*(u_maxtyrA*(y(3))/(Ks_tyrA+y(3)))*y(6);

dy(4) = (u_maxtrpA*y(1))/(Ks_trpA+y(1))*(y(4));

dy(5) = (u_maxmetA*y(2))/(Ks_metA+y(2))*(y(5));

dy(6) = (u_maxtyrA*y(3))/(Ks_tyrA+y(3))*(y(6));

% note: For easy coding and checking purposes, all this equations are exactly the same as the one shown in equation (1), (2), and (3) for dy(1), dy(2), and dy(3) respectively, with also the same arrangements of each equation term within them.

% and Please also note: these equation has been derived with direct simple substitution of equations, where equation (4), (5), (6), (7), (8), (9), (10), (11), (12), (13), (14) and (15) have been substituted into the equation (1), (2) and (3).

%if you want, you can choose to plot diagrams and display the predicting dynamics within the m.file or manually plot the arrays of your interests in command window

%For example, for this project, we are interesting in predicting the ODs of each auxotrophs, thus, we manually plotted values in y(4), y(5), and y(6) (which again, are the OD values of each auxotrophs) agaisnt time, by typing the following in the command window after run the ODE45 functions:

%>>plot(X,Y(:,4:6))



End of the m.file

Simulation Results, Analysis and Future Work

When the Matlab Code was run, it generated a graphic result predicting the population dynamics without inducing, shown below:


Figure 7. A comparison of model predictions versus empirical measurements (n=3) of each auxotroph’s growth in a co-culture.

As shown in the figure above, this model can give reasonable predictions though still can be further modified for better accuracy. For our future work, we will refine our model with more experimental data. This can be accomplished by running the amino acid experiment data points of interest. Alternatively, we can use more direct methods of amino acid measurement, like HPLC analysis.

This code can also be adapted to predict the population dynamics of our cocultures when tuning effects are introduced. This is accomplished by by generating six new functions to describe the amino acid dynamics of each monoculture. We can then use this model to decide the amount of tuning (by adding in different amount of inducer) needed to obtain a desired population of each auxotroph.

References

[1] Shuler, ML; Kargi, F; Bioprocess Engineering Basic Concepts, 2002 2nd edition. Prentice Hall PTR.

[2] Kerner A; Park J; Williams A; Lin XN; A Programmable Escherichia coli Consortium via Tunable Symbiosis, 2012. PLoS ONE 7(3): e34032. doi:10.1371/journal.pone.0034032