# Team:UC Chile/Cyanolux/Modelling

(Difference between revisions)
 Revision as of 02:46, 27 October 2012 (view source)← Older edit Latest revision as of 03:53, 27 October 2012 (view source)Maxfelis (Talk | contribs) (4 intermediate revisions not shown) Line 12: Line 12: It can be adjusted to take as input the time on which enzymatic products concentration peaks , or to maximize an intermediate metabolite that depends not just on production but also consumption. It can be adjusted to take as input the time on which enzymatic products concentration peaks , or to maximize an intermediate metabolite that depends not just on production but also consumption. Finally, its logical design can be applied to any other organism that exhibits circadian oscillation. Finally, its logical design can be applied to any other organism that exhibits circadian oscillation. +
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What´s inside the box?

What´s inside the box?

- We took all the microarray data available of promoter of Synechocystis grown on normal conditions. Conveniently, these arrays already have a filter: they only contain cycling promoters +
We took all the microarray data available of promoter of Synechocystis grown on normal conditions. Conveniently, these arrays already have a filter: they only contain cycling promoters + + + + + + + + + + + + + + + + + + + The microarray data points were adjusted by a least squares algorithm into sine functions for each promoter. The microarray data points were adjusted by a least squares algorithm into sine functions for each promoter. Line 29: Line 57: This equation accounts for protein production and its included into this form of universal balance equation: This equation accounts for protein production and its included into this form of universal balance equation: - equa2 +
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Where HL is protein´s half life Where HL is protein´s half life +
The resulting plot of this equation predicts protein concentration over time. The resulting plot of this equation predicts protein concentration over time. - - For example (plot screenshot) As a complement, we developed a list of relative promoter strengths. We took one promoter present in all datasets to make a relative scale, then,  we transformed it to an absolute scale using a promoter quantitatively characterized in literature by comparing it to the relative scale promoter. As a complement, we developed a list of relative promoter strengths. We took one promoter present in all datasets to make a relative scale, then,  we transformed it to an absolute scale using a promoter quantitatively characterized in literature by comparing it to the relative scale promoter. Line 44: Line 79:

Applying the model: Synechocystis promoters

Applying the model: Synechocystis promoters

- We applied our model to solve the question of which promoters should we use to meet our strategy (http://2012.igem.org/Team:UC_Chile/Cyanolux/Project_short#Strategy) goals + We applied our model to solve the question of which promoters should we use to meet our [http://2012.igem.org/Team:UC_Chile/Cyanolux/Project_short#Strategy strategy goals] The results are shown below: The results are shown below: - Why? - - To express bioluminescence under circadian oscillation it was essential to identify the ideal promoters that could yield highest substrate concentrations at the exact time our biolamp is on i.e. by night. - - Based on literature (ref) we found that suitable promoters to start our model were sigE (sll1689), Transaldolase (slr1793) and Cytochrome aa3 (sll1898). - - Therefore, in the following section we explain how to model the oscillation in terms of product generation under three circadian promoters: sigE (sll1689), Transaldolase (slr1793) and Cytochrome aa3 (sll1898). - - - Methodology - - Product generation by promoter - - From known sigE (sll1689) production data we could build up a model using MatLab software.  (Please check our script!). - - Basically, what we have after data adjustment is a sine function consistent with the oscillatory nature of the circadian rhythm. - - Generation = C*sen(2π(t-(peak-6))/12)+1 - - The C parameter was optimized using Excel Solver’s algorithm of least squares. - - For Transaldolase (slr1793) and Cytochrome aa3 (sll1898) a microarray data was adjusted (using the same algorithm of least squares) to obtain: Promoter Peak hour Promoter Peak hour Line 132: Line 145: MatLab result MatLab result + Finally we had the protein concentrations at different times - Finally we have the protein concentrations at different times + This combination of promoters was the one that best fits our strategy: Maximize substrate concentration during dusk hours - + - + - The files for the following models are: + - + - [[Digitize2.m]] + - [[UC-chileDiffs.m]] + - + - The following are the models we obtained by MatLab + - + - + - [[File:ucchile111111.png|500px|center]] + - + - [[File:222222.png|500px|center]] + - + - [[File:33333333.png|500px|center]] + - + - [[File:444444.png|500px|center]] + - + - [[File:555555.png|500px|center]] + - + - [[File:666666.png|500px|center]] + - + - And this is the best model because it yields highest substrate concentration at the desired time (12 hr) + - [[File:ucchile111111.png|500px|center]] +

## Latest revision as of 03:53, 27 October 2012

Project: Luxilla - Pontificia Universidad Católica de Chile, iGEM 2012