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Team:Grenoble/Modeling/Amplification/Stochastic/results
From 2012.igem.org
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<ul><ul><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" /> Is the sensibility given by stochastic modeling the same that in ODE modeling ? | <ul><ul><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" /> Is the sensibility given by stochastic modeling the same that in ODE modeling ? | ||
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| - | <h1><img src="https://static.igem.org/mediawiki/2012/ | + | <h1><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" /> Sensibility </h1> |
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The ODE modeling gave 10<SUP>-6</SUP> mol/L of CAMP<SUB>i</SUB> as the sensibility. This means, if we have 10<SUP>-6</SUP> mol/L of CAMP at the initial point, the system will turn on. | The ODE modeling gave 10<SUP>-6</SUP> mol/L of CAMP<SUB>i</SUB> as the sensibility. This means, if we have 10<SUP>-6</SUP> mol/L of CAMP at the initial point, the system will turn on. | ||
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Revision as of 11:48, 19 September 2012
Goal
In this part we would like to answer 3 questions thanks to the stochastic modeling.
How much time do we need to wait to get a response ?
Is the sensibility given by stochastic modeling the same that in ODE modeling ?
What is the part of false positives ?
Time
Sensibility
The ODE modeling gave 10-6 mol/L of CAMPi as the sensibility. This means, if we have 10-6 mol/L of CAMP at the initial point, the system will turn on.
But this result is given by a deterministic analysis. What happens if we take into account the random phenomena of the bacterium ? Is the sensibility still so good ?
To perform that study, we use a Gillespie Algorithm to add randomness in our system. We want to obtain the evolution of the output signal (CA or GFP) depending on the concentration of the input signal (CAMPi) after 6h40 (400 min).
To get that graph we simulate the algorithm hundreed times for each concentration of CAMPi (1, 5, 10, …, 100 000 molecules).
We can compare the graph obtained with the one from the ODE modeling.
