Team:NTU Taida/M2

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In model 1, we follow the model proposed in DREAM[1](Dialogue for Reverse Engineering Assessments and Methods,a competition of computational biology).
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 : </p>
Here is the model of competition : </p>
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<center><img src="images/h3.jpg" > </p></center>
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<center><img src="https://static.igem.org/mediawiki/2014/0/01/NTU_Taida_H3.jpg" > </p></center>
<center><u>Fig 2[1] diagram of model 1</u></center>
<center><u>Fig 2[1] diagram of model 1</u></center>
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<center><img src="images/h4.jpg" > </p></center>
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<center><img src="https://static.igem.org/mediawiki/2014/7/7e/NTU_Taida_H4.jpg" > </p></center>
<center><u>Fig 3[1] components of model 1</u></center>
<center><u>Fig 3[1] components of model 1</u></center>
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   
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   
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<center><img src="https://static.igem.org/mediawiki/2014/3/3f/NTU_Taida_H5.jpg" > </p></center>
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.
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.
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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.

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