Team:Tsinghua-A/Modeling/GILLESPIE

From 2012.igem.org

(Difference between revisions)
 
(5 intermediate revisions not shown)
Line 13: Line 13:
<link href='http://fonts.googleapis.com/css?family=Oxygen+Mono|Raleway+Dots|Quantico:400' rel='stylesheet' type='text/css'>
<link href='http://fonts.googleapis.com/css?family=Oxygen+Mono|Raleway+Dots|Quantico:400' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="https://2012.igem.org/Team:Tsinghua-A/css/style-original.css?action=raw&ctype=text/css"/>
<link rel="stylesheet" type="text/css" href="https://2012.igem.org/Team:Tsinghua-A/css/style-original.css?action=raw&ctype=text/css"/>
-
+
<style>
 +
.navigationButtonA:hover
 +
{
 +
color:rgb(12,117,34)!important;
 +
}
 +
</style>
<script type="text/javascript" src="https://static.igem.org/mediawiki/2012/c/c8/THU-AIndex.txt"></script>
<script type="text/javascript" src="https://static.igem.org/mediawiki/2012/c/c8/THU-AIndex.txt"></script>
</head>
</head>
Line 22: Line 27:
<div id="topTitle">
<div id="topTitle">
-
<h2 id="titleText" style="color:rgb(137,202,154);">Tsinghua-A::Modeling</h2>
+
<h2 id="titleText" style="color:rgb(137,202,154);">Tsinghua-A::Modeling::<span style="color: rgb(12,117,34);">GILLESPIE Algorithm</span></h2>
</div>
</div>
<div id="topWrapper">
<div id="topWrapper">
Line 34: Line 39:
-
     <h2 class="textTitle" >Gillespie algorithm:</h2>
+
     <h2 class="textTitle" >Gillespie algorithm</h2>
<div class="entry-content">
<div class="entry-content">
<p>the reverse process of the gene between the two Loxp sites could not be well described by the DDE or ODE equations since after the reverse process, the gene will have chance to flip again. This process is a discrete, stochastic process instead of a continuous, deterministic process. To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.<a class="textLink" href="https://2012.igem.org/Team:Tsinghua-A/Modeling/GILLESPIE/part1">readmore</a>
<p>the reverse process of the gene between the two Loxp sites could not be well described by the DDE or ODE equations since after the reverse process, the gene will have chance to flip again. This process is a discrete, stochastic process instead of a continuous, deterministic process. To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.<a class="textLink" href="https://2012.igem.org/Team:Tsinghua-A/Modeling/GILLESPIE/part1">readmore</a>
Line 45: Line 50:
<p id="2" class="jump" style=" position: absolute;margin-top:-170px;"/>
<p id="2" class="jump" style=" position: absolute;margin-top:-170px;"/>
<div id="post" class="post" >
<div id="post" class="post" >
-
     <h2 class="textTitle" style="font-size:38px;">Our model using Gillespie algorithm</h2>
+
     <h2 class="textTitle" style="font-size:38px;">Our model</h2>
<div class="entry-content">
<div class="entry-content">
<p>In our model , we use Gillespie algorithm to simulate the process of inversion. Here we use   
<p>In our model , we use Gillespie algorithm to simulate the process of inversion. Here we use   

Latest revision as of 21:23, 26 September 2012

Tsinghua-A::Modeling::GILLESPIE Algorithm

Gillespie algorithm

the reverse process of the gene between the two Loxp sites could not be well described by the DDE or ODE equations since after the reverse process, the gene will have chance to flip again. This process is a discrete, stochastic process instead of a continuous, deterministic process. To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.readmore

Our model

In our model , we use Gillespie algorithm to simulate the process of inversion. Here we use Markov chain to describe the process of inversion. We assume that the state ‘A’ , ‘B’ , ‘C’ , ‘D’ , is the original state , the state after inversion , the Intermediate state when A forms holiday junction, the Intermediate state when B forms holiday junction respectively. readmore

A better model

1000 genes which are in state ‘A’ are seen as a whole in the above model. To make the simulation more convincing, we then simulate the trajectory of every single molecule. After that we synthesize all trajectories to get the final result.readmore