Difference between revisions of "Team:NCTU Formosa/Modeling"

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<p>In the modeling part, we discover <font color="#AC1F4A">optimum protein expression time</font>.</p>
 
<p>In the modeling part, we discover <font color="#AC1F4A">optimum protein expression time</font>.</p>
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<p>we use <font color="#AC1F4A">Hill-function based model</font> : </p>
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<p>We use <font color="#AC1F4A">Hill-function based model</font> : </p>
 
<div class="image">
 
<div class="image">
 
<img src="https://static.igem.org/mediawiki/2015/e/eb/Nctu_formosa_model_equation.png" height="300px">
 
<img src="https://static.igem.org/mediawiki/2015/e/eb/Nctu_formosa_model_equation.png" height="300px">
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<p>In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the <font color="#AC1F4A">genetic algorithm (GA)</font> in MATLAB.</p>
 
<p>In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the <font color="#AC1F4A">genetic algorithm (GA)</font> in MATLAB.</p>
  
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<p>To achieve this, we need to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offer focus of the present difficulty, also known as the <font color="#AC1F4A">Estimation of Model Parameters Challenge</font>.</p>
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<p>To achieve this, we needed to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offered focus of the present difficulty, also known as the <font color="#AC1F4A">Estimation of Model Parameters Challenge</font>.</p>
  
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<p>The optimum parameters which were calculated by GA can help us to simulate the optimum protein expression.</p>
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<p>By using the differential function which was derived from these optimum parameters which were calculated by GA can help us to simulate the optimum protein expression. </p>
  
 
<p>The graph of simulated protein expression versus time were drawn (Fig.1) (Fig.2) (Fig.3). Thus, we can find the <font color="#AC1F4A">optimum protein expression time</font>.
 
<p>The graph of simulated protein expression versus time were drawn (Fig.1) (Fig.2) (Fig.3). Thus, we can find the <font color="#AC1F4A">optimum protein expression time</font>.
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Compared with the simulated protein expression (orange curve), our experimental data (blue curve) quite fit the simulation.
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However, the simulated protein expression curve is slower than the experimental curve by one hour. Thus, to find the most exact optimum protein expression time, we infer that subtracting one hour of the optimum protein expression time would be correct.
 
</p>
 
</p>
  
 
<div class="image">
 
<div class="image">
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<img src="https://static.igem.org/mediawiki/2015/f/fa/Nctu_formosa_her_bc.png" height="300px"><br><br>
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<img src="https://static.igem.org/mediawiki/2015/3/3f/NCTU_FORMOSA_SCFV_BC.PNG" height="300px"><br><br>
 
Figure 1.
 
Figure 1.
 
From this graph, the protein expression reaches peak after growing about 15 hours.
 
From this graph, the protein expression reaches peak after growing about 15 hours.

Revision as of 03:01, 19 September 2015

Modeling

In the modeling part, we discover optimum protein expression time.

We use Hill-function based model :

In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the genetic algorithm (GA) in MATLAB.

To achieve this, we needed to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offered focus of the present difficulty, also known as the Estimation of Model Parameters Challenge.

By using the differential function which was derived from these optimum parameters which were calculated by GA can help us to simulate the optimum protein expression.

The graph of simulated protein expression versus time were drawn (Fig.1) (Fig.2) (Fig.3). Thus, we can find the optimum protein expression time. However, the simulated protein expression curve is slower than the experimental curve by one hour. Thus, to find the most exact optimum protein expression time, we infer that subtracting one hour of the optimum protein expression time would be correct.



Figure 1. From this graph, the protein expression reaches peak after growing about 15 hours. This means that the E. Cotector can have maximum efficiency at this point.



Figure 2. From this graph, the protein expression reaches peak after growing about 18 hours. This means that the E. Cotector can have maximum efficiency at this point.



Figure 3. From this graph, the protein expression reaches peak after growing about 16 hours. This means that the E. Cotector can have maximum efficiency at this point.


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