Team:Grenoble/Projet/Modelling

From 2011.igem.org

(Difference between revisions)
Line 22: Line 22:
    <h1>Modelling</h1>
    <h1>Modelling</h1>
    <p>
    <p>
-
In the particular case of our circuit and final use of the bacteria, the modelling team had to check
+
The modelling team is responsible for:
-
the effectiveness of our circuit of course, but also had to give the specificities (size, number of  
+
<ol>
-
bacteria, IPTG gradient specificities) of the measuring device we intend to produce.
+
<li><strong>Verifying that the designed genetic circuit elicits the desired behavior in <i>E coli.</i></strong>
 +
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).</li>
 +
 +
<li><strong>Providing specifications for the quantification device (size, number of bacteria, IPTG gradient).</strong>
 +
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.</li>
 +
</ol>
</p>
</p>
     </div>
     </div>
Line 30: Line 40:
<h2>Table of content</h2>
<h2>Table of content</h2>
<p>
<p>
-
In the next pages we first expose technically which algorithms we used for both deterministic modelling and  
+
In the following pages, we detail the development of the deterministic and stochastic models,
-
stochastic modelling and explain our MATLAB scripts (available
+
together with their dynamical analysis. Solving numerically these systems required tricky calculations,
-
<a href="http://igemgrenoble-files.perso.sfr.fr/2011/MATLAB_Archives/">here</a>) in the hope it could help
+
since they evolve both in time and space. To help future teams with similar calculations, we explain
-
future modelling teams. <br/>We exposed the results in the last section, for anyone who is interested in
+
in details the algorithms that were used and provide the scripts <a href="http://igemgrenoble-files.perso.sfr.fr/2011/MATLAB_Archives/">here</a>.
-
the results only<br/><br/>
+
The modelling results are described in the last section.
 +
 +
<br/><br/>
  <a href="https://2011.igem.org/Team:Grenoble/Projet/Modelling/Deterministic" ><img class="icon" src="https://static.igem.org/mediawiki/2011/b/b4/Icon_deterministic.png"/></a>
  <a href="https://2011.igem.org/Team:Grenoble/Projet/Modelling/Deterministic" ><img class="icon" src="https://static.igem.org/mediawiki/2011/b/b4/Icon_deterministic.png"/></a>
     
     

Revision as of 22:28, 27 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.

Retrieved from "http://2011.igem.org/Team:Grenoble/Projet/Modelling"