Difference between revisions of "Team:UESTC-China/Model"

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<p>We combined modeling with human practice and established the layout model of devices to provide a solution for human practice. Inspired by the placement of dustbins now, firstly we carried out the layout of the dustbin using the multi-objective programming model, and then carried out the layout of the device. Finally, we made the dustbin and the device as close as possible, thus increasing the probability of residents throwing expired drugs. The model reduced costs for our projects and increased the efficiency of processing expired drugs.</p>
 
<p>We combined modeling with human practice and established the layout model of devices to provide a solution for human practice. Inspired by the placement of dustbins now, firstly we carried out the layout of the dustbin using the multi-objective programming model, and then carried out the layout of the device. Finally, we made the dustbin and the device as close as possible, thus increasing the probability of residents throwing expired drugs. The model reduced costs for our projects and increased the efficiency of processing expired drugs.</p>
 
             <a href="https://2019.igem.org/Team:UESTC-China/Model4" class="myclick">please click here to read more</a>
 
             <a href="https://2019.igem.org/Team:UESTC-China/Model4" class="myclick">please click here to read more</a>

Revision as of 01:56, 7 October 2019

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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.

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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.

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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.

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We combined modeling with human practice and established the layout model of devices to provide a solution for human practice. Inspired by the placement of dustbins now, firstly we carried out the layout of the dustbin using the multi-objective programming model, and then carried out the layout of the device. Finally, we made the dustbin and the device as close as possible, thus increasing the probability of residents throwing expired drugs. The model reduced costs for our projects and increased the efficiency of processing expired drugs.

please click here to read more
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