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

 
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<div class="content"><p>In the modeling part, we discover optimum protein production time.</p>
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<p>Firstly, we use hill-function based model:</p>
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<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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<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 find the optimum parameters of,
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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>
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we use the genetic algorithm in MATLAB.
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We want to characterize the actual kinetics of this Hill-function based model that accurately reflects protein production time.
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To achieve this purpose, 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 Estimation of Model Parameters Challenge.</p>
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<p>When we have the simulated protein production rate, the graph of protein production versus time can be drawn (Fig.1) (Fig.2) (Fig.3). Thus, we can find the optimum protein production time
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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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Compared with the simulated protein production rate of time, our experiment data quite fit the simulation.</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>
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<p>The graph of simulated protein expression versus time were drawn Figure 1, 2 and 3. Thus, we can find the <font color="#AC1F4A">optimum protein expression time</font>.
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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.
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<img src="https://static.igem.org/mediawiki/2015/9/90/Nctu_formosa_model_figure1.png" height="300px"><br><br>
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<img src="https://static.igem.org/mediawiki/2015/1/15/NCTU_Formosa_modeling_2.png" height="300px"><br><br>
 
Figure 1.
 
Figure 1.
 
From this graph, the protein expression reaches peak after growing about 18 hours.
 
From this graph, the protein expression reaches peak after growing about 18 hours.
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This means that the E. Cotector can have maximum efficiency at this point.<br><br>
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
  
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<img src="https://static.igem.org/mediawiki/2015/c/cb/Nctu_Formosa_model_figure2.png" height="300px"><br><br>
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<img src="https://static.igem.org/mediawiki/2015/5/55/NCTU_Formosa_modeling_3.png" height="300px"><br><br>
 
Figure 2.
 
Figure 2.
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From this graph, the protein expression reaches peak after growing about 15 hours.
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From this graph, the protein expression reaches peak after growing about 16 hours.
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This means that the E. Cotector can have maximum efficiency at this point.<br><br>
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
  
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<img src="45" 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 3.
 
Figure 3.
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From this graph, the protein expression reaches peak after growing about 16 hours.
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From this graph, the protein expression reaches peak after growing about 11 hours.
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This means that the E. Cotector can have maximum efficiency at this point.
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
 
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Latest revision as of 03:33, 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 Figure 1, 2 and 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 18 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 16 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 11 hours. This means that the E.Cotector can have maximum efficiency at this point.



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