Difference between revisions of "Team:UESTC-China/Model"
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<a href="https://2019.igem.org/Team:UESTC-China/Model1">Degradation of CrpP enzyme system model</a> | <a href="https://2019.igem.org/Team:UESTC-China/Model1">Degradation of CrpP enzyme system model</a> | ||
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<a href="https://2019.igem.org/Team:UESTC-China/Model2">Hardware</a> | <a href="https://2019.igem.org/Team:UESTC-China/Model2">Hardware</a> | ||
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<a href="https://2019.igem.org/Team:UESTC-China/Model3">Quorum sensing model</a> | <a href="https://2019.igem.org/Team:UESTC-China/Model3">Quorum sensing model</a> | ||
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<a href="https://2019.igem.org/Team:UESTC-China/Model4">XXX</a> | <a href="https://2019.igem.org/Team:UESTC-China/Model4">XXX</a> | ||
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Revision as of 07:16, 6 October 2019
In order to find the optimal degradation conditions for CrpP enzyme, we found the main factors affecting the degradation rate from the paper.
Firstly, we used the Plackett-Burman design to screen factors and got the important factors. Secondly, the Box-Behnken design was used in the experimental design. According to the experimental results, the response surface equation was obtained. Finally, after obtaining the extreme value of the regression equation, it can be verified by experiments.
In order to combine hardware with synthetic biology, we need to obtain the relationship between the concentration of ciprofloxacin and the corresponding voltage, and predict the voltage according to the concentration of ciprofloxacin.
Considering the complexity of the mechanism of intracellular promoter, we analyzed it from a macro perspective. By establishing BP neural network and letting it constantly learn the concentration of ciprofloxacin and the corresponding voltage values we test, we continuously adjusted the input layer, the hidden layer. Finally, we can obtain the appropriate weights and the relationship between the voltage and the concentration of ciprofloxacin.
To further understand, predict, and control the behavior of engineered microbial group effect, we modeled the group effects based on the entire process of crpp enzyme production.
The model involves a wide range of biological and physical processes, such as diffusion, binding and so on. Through the establishment and solution of differential equations, we can use this model to predict the relationship between the production of Crpp enzyme and the external concentration of AHL.
(1) School of Life Sciences, University of Electronic Science and Technology of China.
(2) Office of Educational Administration, University of Electronic Science and Technology of China.
(3) Office of Students' Affairs, University of Electronic Science and Technology of China.
(4) Plant Genome Engineering Lab, University of Electronic Science and Technology of China.
(5) Protein Engineering Lab, University of Electronic Science and Technology of China.
(6) College Of Life Sciences, Sichuan University.