Team:Grenoble/Projet/Modelling

From 2011.igem.org

(Difference between revisions)
Line 7: Line 7:
<html>
<html>
-
<!--
 
-
<img src="http://www.clickartists.org/clicksite/html/images/square.jpg"/>
 
 +
<img src="http://www.clickartists.org/clicksite/html/images/square.jpg"/>
 +
<!--
Si vous éditez la page commencez par décommenter ces lignes, publier, PUIS commencer à faire ce que vous avez à faire et quand vous avez fini de publier remettez en commentaire.
Si vous éditez la page commencez par décommenter ces lignes, publier, PUIS commencer à faire ce que vous avez à faire et quand vous avez fini de publier remettez en commentaire.

Revision as of 15:38, 28 October 2011

Grenoble 2011, Mercuro-Coli iGEM


Modelling

The modelling team is responsible for:

  1. Verifying that the designed genetic circuit elicits the desired behavior in E coli. A deterministic model of the circuit has been developed in order to predict the dynamical behavior of the circuit in the presence of a concentration gradient of IPTG and various concentrations of Mercury or aTc (anhydrotetracycline).
  2. Providing specifications for the quantification device (size, number of bacteria, IPTG gradient). The apparition of the red line indicating the presence and quantity of Mercury or aTc is sensitive to fluctuations in the concentration of these molecules and IPTG. We took into account these fluctuations in a stochastic version of the circuit model, which we have used to determine the specifications of our device.

Table of content

In the following pages, we detail the development of the deterministic and stochastic models, together with their dynamical analysis. Solving numerically these systems required tricky calculations, since they evolve both in time and space. To help future teams with similar calculations, we explain in details the algorithms that were used and provide the scripts here. The modelling results are described in the last section.

Construction of the model :
Establishment of the equation Toggle switchQuorum sensing
Our algorithms


Stochastic Modelling :
Sensitivity to noise
Gillespie algorithm
Mean, standard deviation and statistical properties

Parameters
Table of parameters
Sensitivity to parameters


You can find the results of our modelling results in the results page.