Team:Grenoble/Modeling/Amplification/Stochastic/results

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Thanks to the deterministic modeling we can have an estimation of the time needed to get an answer in differentt proportions. In the graph below you can observe the evolution of the output signal through time for an initial concentration of CAMP of 10<SUP>-3</SUP> mol/L.
Thanks to the deterministic modeling we can have an estimation of the time needed to get an answer in differentt proportions. In the graph below you can observe the evolution of the output signal through time for an initial concentration of CAMP of 10<SUP>-3</SUP> mol/L.
<center><img src="https://static.igem.org/mediawiki/2012/8/81/Time_det.png" alt="" /></center>
<center><img src="https://static.igem.org/mediawiki/2012/8/81/Time_det.png" alt="" /></center>
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After 200 minutes we get half of the maximum output signal. If we wait 200 minutes more we reach 80% of the output signal.
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Therefore we can assess the time needed to get a correct answer as 400 minutes for the time being. We then have to check if that time is still appropriate when we take into accout the randomness.
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Revision as of 12:29, 19 September 2012

iGEM Grenoble 2012

iGEM Grenoble iGEM
iGEM 2012
main page
Project

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 sensitivity given by stochastic modeling the same as in ODE modeling ?
      What is the part of false positives ?
Thanks to those 3 questions we will be able to establish if our device is still performing when we take into account random variations.

Time


The purpose of our device is to act more quickly than current techniques to detect the Golden Staph. Therefore we would like to evaluate the time needed to get an answer. We need to establish a time not to small to avoid false negatives and not to long to avoid false positives and to be performing.

In this first part we try to determine that time and in the third part we will analyse if it still be a good scale of time with respect to false positives.

Thanks to the deterministic modeling we can have an estimation of the time needed to get an answer in differentt proportions. In the graph below you can observe the evolution of the output signal through time for an initial concentration of CAMP of 10-3 mol/L.
After 200 minutes we get half of the maximum output signal. If we wait 200 minutes more we reach 80% of the output signal. Therefore we can assess the time needed to get a correct answer as 400 minutes for the time being. We then have to check if that time is still appropriate when we take into accout the randomness.

Sensibility


The ODE modeling gave 10-6 mol/L of CAMPi as the sensitivity. 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 sensitivity 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.
We can notice that, when we add stochastic variations to the model, the sensitivity decreases from 10-5,5 mol/L to 3,3.10-5 mol/L. In other words, we loose a sensitivity of 2,98.10-5 mol/L, that is 18 000 molecules of CAMPi. However this loss of sensitivity is low and doesn't penalize our device at all.

False positives

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