Team:NTU Taida/M2

From 2014.igem.org

(Difference between revisions)
 
Line 24: Line 24:
   </head>
   </head>
   <body>
   <body>
-
     <nav class="navbar navbar-default navbar-fixed-top" role="navigation" style="padding-top:20px">
+
     <nav class="navbar navbar-default navbar-fixed-top" role="navigation">
   <div class="container-fluid">
   <div class="container-fluid">
     <!-- Brand and toggle get grouped for better mobile display -->
     <!-- Brand and toggle get grouped for better mobile display -->
Line 34: Line 34:
         <span class="icon-bar"></span>
         <span class="icon-bar"></span>
       </button>
       </button>
-
       <a href="https://2014.igem.org/Team:NTU_Taida"><img id="logo" height="50" src="https://static.igem.org/mediawiki/2014/1/19/NTU_Taida_logo.jpg"></a>
+
       <a href="https://2014.igem.org/Team:NTU_Taida"><img id="logo" width="70px" src="https://static.igem.org/mediawiki/2014/1/19/NTU_Taida_logo.jpg"></a>
     </div>
     </div>

Latest revision as of 01:05, 18 October 2014

NTU-Taida

Model 1:

In model 1, we follow the model proposed in DREAM[1](Dialogue for Reverse Engineering Assessments and Methods,a competition of computational biology). Here is the model of competition :

Fig 2[1] diagram of model 1

Fig 3[1] components of model 1
In figure 2,it illustrates a example of gene regulatory network, which the component are listed in figure 3.In the model, the transcription process is modeled using a single rate equation[1]. The rate equation.The rate equation is expressed as a sum of the transcriptional activity ( as) of all the activators in that promoter region multiplied by the product of the transcriptional activity ( rs ) of all the repressor binding sites in the same region. For example the transcription rate of v4_mrna in Fig. 3 will be modeled as pro4_strength * as4 * rs2 , where

And the rate of production of protein "p4" is given by linear rate equation rbs4_strength * v4_mrna. One of reason why we choose this model is the component of the transcription rate and translation rate including the term of promoter strength and rbs strength, which have potential to combine with the experimental data of previous iGEM teams’ contribution[2][3], and then can build connection between the computational model and experimental data.