Team:Aberdeen Scotland/Modeling

From 2010.igem.org

(Difference between revisions)
m
 
(30 intermediate revisions not shown)
Line 1: Line 1:
-
== Our Ordinary Differential Equations ==
+
{{:Team:Aberdeen_Scotland/css}}
 +
{{:Team:Aberdeen_Scotland/Title}}
 +
<html>
 +
<h1>Introduction to the Modelling of the ayeSwitch</h1>
 +
<p>This page is an introduction to the different equations and techniques that we used to design and predict the behaviour of the ayeSwitch.
 +
<br>
 +
</p>
 +
<br>
 +
<a href="https://2010.igem.org/Team:Aberdeen_Scotland/Equations"><h3><font color="blue">Equations</font></h3></a>
 +
<p>In this section we describe the process of developing a basic mathematical model for the ayeSwitch based on the promotion and inhibition behaviour necessary for mutual repression.  We developed a set of four differential equations, one to model each of the two mRNAs and two proteins that are the active components of our system.</p>
 +
<br>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Fixed_Points"><font color="blue">Nullclines and Fixed Points</font></a></h3>
 +
<p>In this section we describe how we used fixed point analysis to predict the equilibrium state(s) of the ayeSwitch system for different parameters. Three or more equilibrium points will give us bistability and the possibility of switching. </p>
 +
<br>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Bifurcation"><font color="blue">Bifurcation and Stability</font></a></h3>
 +
<p>Bifurcation analysis allows us to track the number and relative position of the equilibrium points of our system for different parameters. This section describes in detail how bifurcation analysis can help us determine the optimal range for our parameters. </p>
 +
<br>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Stochastic_Model"><font color="blue">Stochastic Model</font></a></h3>
 +
<p>There are two ways to model our system - deterministically and stochastically. Both methods have their advantages and disadvantages depending on the system in question. In our system, it was more beneficial to model the system stochastically due to the low numbers of molecules involved. This section describes in detail both methods, their advantages and disadvantages and how they are used to model our system.</p>
 +
<br>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Probability"><font color="blue">Parameter Space Analysis</font></a></h3>
 +
<p>This section describes in detail how we analysed the parameter space for our system. The results of this will show when bistability is possible and when it is not. Using this information, we can determine the optimal parameter ranges for our system. </p>
 +
<br>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Curve_Fitting"><font color="blue">Determination of the Hill coefficient n<sub style="font-size:10px">1</sub></font></a></h3>
 +
<p>The Hill coefficient relating to the CFP/MS2 stem loop association is assumed to be around 2 due to the number of stem loops present. This section details the method we used to calculate this value more accurately, and what the result means in terms of the parameter space analysis.</p>
 +
<h3><a href="https://2010.igem.org/Team:Aberdeen_Scotland/Evolution"><font color="blue">Directed Evolution</font></a></h3>
 +
<p>Here we describe one of the possible ways to improve our switch which we would have been able to attempt given more time.  The results show an optimized version of our original system.  </p>
-
[[Image:dm1.jpg]]
+
<br><br>
 +
<hr>
 +
<table class="nav">
 +
<tr>
 +
<td>
 +
<a href="https://2010.igem.org/Team:Aberdeen_Scotland/Parts"><img src="https://static.igem.org/mediawiki/2010/8/8e/Left_arrow.png">&nbsp;&nbsp;Return to Parts Submitted to the Registry</a>
 +
</td>
 +
<td align="right">
 +
<a href="https://2010.igem.org/Team:Aberdeen_Scotland/Equations">Continue to Equations&nbsp;&nbsp;<img src="https://static.igem.org/mediawiki/2010/3/36/Right_arrow.png"></a>
 +
</td>
 +
</tr>
 +
</table>
-
This is the equation for the rate of change of the mRNA that is transcribed
+
</html>
-
from the galactose promoter. The three terms represent production, degra-
+
{{:Team:Aberdeen_Scotland/Footer}}
-
dation, and dilution respectively.
+
-
 
+
-
----
+
-
 
+
-
[[Image:dGFP.jpg]]
+
-
 
+
-
This is the equation for the rate of change of protein that is translated from
+
-
the mRNA for GFP. The three terms represent production, degradation, and
+
-
dilution respectively.
+
-
 
+
-
----
+
-
 
+
-
[[Image:dm2.jpg]]
+
-
 
+
-
This is the equation for the rate of change of the mRNA that is transcribed
+
-
from the copper promoter. The three terms represent production, degrada-
+
-
tion, and dilution respectively.
+
-
 
+
-
----
+
-
 
+
-
[[Image:dCFP.jpg]]
+
-
 
+
-
This is the equation for the rate of change of protein that is translated from
+
-
the mRNA for CFP. The three terms represent production, degradation, and
+
-
dilution respectively.
+

Latest revision as of 20:23, 27 October 2010

University of Aberdeen - ayeSwitch - iGEM 2010

Introduction to the Modelling of the ayeSwitch

This page is an introduction to the different equations and techniques that we used to design and predict the behaviour of the ayeSwitch.


Equations

In this section we describe the process of developing a basic mathematical model for the ayeSwitch based on the promotion and inhibition behaviour necessary for mutual repression. We developed a set of four differential equations, one to model each of the two mRNAs and two proteins that are the active components of our system.


Nullclines and Fixed Points

In this section we describe how we used fixed point analysis to predict the equilibrium state(s) of the ayeSwitch system for different parameters. Three or more equilibrium points will give us bistability and the possibility of switching.


Bifurcation and Stability

Bifurcation analysis allows us to track the number and relative position of the equilibrium points of our system for different parameters. This section describes in detail how bifurcation analysis can help us determine the optimal range for our parameters.


Stochastic Model

There are two ways to model our system - deterministically and stochastically. Both methods have their advantages and disadvantages depending on the system in question. In our system, it was more beneficial to model the system stochastically due to the low numbers of molecules involved. This section describes in detail both methods, their advantages and disadvantages and how they are used to model our system.


Parameter Space Analysis

This section describes in detail how we analysed the parameter space for our system. The results of this will show when bistability is possible and when it is not. Using this information, we can determine the optimal parameter ranges for our system.


Determination of the Hill coefficient n1

The Hill coefficient relating to the CFP/MS2 stem loop association is assumed to be around 2 due to the number of stem loops present. This section details the method we used to calculate this value more accurately, and what the result means in terms of the parameter space analysis.

Directed Evolution

Here we describe one of the possible ways to improve our switch which we would have been able to attempt given more time. The results show an optimized version of our original system.



  Return to Parts Submitted to the Registry Continue to Equations  

Back to the Top

Retrieved from "http://2010.igem.org/Team:Aberdeen_Scotland/Modeling"