Team:KAIST Korea/Project Modeling

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<a href="#1st"><span id="tab"><b>Cell Growth Curve</b> </span></a>
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<a href="#2nd"><span id="tab"><b>Proof of concept</b></span></a>
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<a href="#3rd"><span id="tab"><b>Auto Regulation</b></span></a>
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<li style="list-style-type:square;font-size:14px;font-weight:bold;">Mathematical model</li>
<li style="list-style-type:square;font-size:14px;font-weight:bold;">Mathematical model</li>
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<li style="list-style-type:square;font-size:14px;font-weight:bold;">Mathematical model</li>
<li style="list-style-type:square;font-size:14px;font-weight:bold;">Mathematical model</li>

Revision as of 16:28, 26 October 2012

KAIST Korea 2012 iGEM

Project : Modeling
Cell Growth Curve Proof of concept Auto Regulation

Modeling

Computational modeling of our project

Cell Growth Curve

Cell growth can be modeled using Logistic differential equation as shown below.


When we solve this equation with appropriate parameters(using MATLAB), we can get solution curve as shown below. This curve matches with our knowledge about cell growth.





Proof of concept

  • Mathematical model

With GFP and RFP, we want to check whether our system really works or not. Since we cannot consider all the complicated biological phenomena, we assume our system simply follows reaction rate theory and mass balance equations. Reactions below are simplified version of our system. We CONSIDERED the production and degradation of mRNAs and Genes, while we IGNORED the polysome phenomena and any gene regulatory system that occurs in real biological system. Also, we CONSIDERED that there are many copies of plasmids in E.coli and each plasmid can react with invertase to invert their gene sequence.
Because every plasmid have equal probability to react with invertase, we assume plasmids follow uniform distribution. Final assumption is that each plasmids are mutually independent, that is, each plasmid cannot affect invertase reaction of other plasmid.

Using these reaction, we constructed mathematical model of our system as shown below. Pg(gene probability), in our model, represents the number of plasmid which is inverted. And the rate of producing inverted gene is reduced as the remaining non-inverted gene is reduced.


Using MATLAB we can solve these set of differential equations and get solution curve like below.


We also consider concentration of GFP/RFP of cell colony. The solution curve shown below represents that result.




  • Parameter Sensitivity Analysis

We also do parameter sensitivity analysis to find what kind of parameters are critically impact on our system. We define sensitivity coefficient and calculate as paramters vary with some ratio.


And We plot the result using 3d bar graph. As graph represents, some parameters are critical to change the output of our system and some are not.








Auto Regulation

  • Mathematical model

Similar to 'Title2', we can write simplified equation of our system, that is shown below.



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