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

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<h2> Modeling</h2>
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<h4>Note</h4>
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<p>In order to be considered for the <a href="https://2015.igem.org/Judging/Awards#SpecialPrizes">Best Model award</a>, you must fill out this page.</p>
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<div class="title">Modeling</div>
 
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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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<div class="image">
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<img src="https://static.igem.org/mediawiki/2015/e/eb/Nctu_formosa_model_equation.png" height="300px">
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
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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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<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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Here are a few examples from previous teams:
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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.
 
</p>
 
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<ul>
 
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
 
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
 
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</ul>
 
  
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<img src="https://static.igem.org/mediawiki/2015/1/15/NCTU_Formosa_modeling_2.png" height="300px"><br><br>
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Figure 1.
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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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<img src="https://static.igem.org/mediawiki/2015/5/55/NCTU_Formosa_modeling_3.png" height="300px"><br><br>
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Figure 2.
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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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<img src="https://static.igem.org/mediawiki/2015/3/3f/NCTU_FORMOSA_SCFV_BC.PNG" height="300px"><br><br>
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Figure 3.
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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.<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.



Back to Navigation


Go to Safety