Difference between revisions of "Team:USTC/Model"
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<img class="card-img-top" src="https://static.igem.org/mediawiki/2018/5/5d/T--USTC--Model1-Fig2.png" alt="Card image cap"></a> | <img class="card-img-top" src="https://static.igem.org/mediawiki/2018/5/5d/T--USTC--Model1-Fig2.png" alt="Card image cap"></a> | ||
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| − | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href=" | + | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="https://2018.igem.org/Team:USTC/Model/Single_Cell_Model">Single-cell model</a></h3> |
<p class="card-text">Single-cell model is consisted of 22 nonlinear ordinary differential equations. We use Matlab solvers to simulate the change of species concentration along the time. Then we analyze the condition of initial steady state and nicotine sensing state with defined parameters. These results strongly prove the feasibility of our design.</p> | <p class="card-text">Single-cell model is consisted of 22 nonlinear ordinary differential equations. We use Matlab solvers to simulate the change of species concentration along the time. Then we analyze the condition of initial steady state and nicotine sensing state with defined parameters. These results strongly prove the feasibility of our design.</p> | ||
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<img class="card-img-top" src="https://static.igem.org/mediawiki/2018/b/b6/T--USTC--Model2-Fig18.png" alt="Card image cap"></a> | <img class="card-img-top" src="https://static.igem.org/mediawiki/2018/b/b6/T--USTC--Model2-Fig18.png" alt="Card image cap"></a> | ||
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| − | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href=" | + | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="https://2018.igem.org/Team:USTC/Model/System_Analysis">System analysis</a></h3> |
<p class="card-text">This part we analyze the impact of different combinations of promoter strengths and copy numbers to the initial steady states and outputs with nicotine input. Our simulation results present the tendency of initial state change and the signal shifting. According to these phenomena, we can choose the appropriate promoter combinations and plasmid backbones for the realization of our design.</p> | <p class="card-text">This part we analyze the impact of different combinations of promoter strengths and copy numbers to the initial steady states and outputs with nicotine input. Our simulation results present the tendency of initial state change and the signal shifting. According to these phenomena, we can choose the appropriate promoter combinations and plasmid backbones for the realization of our design.</p> | ||
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<img class="card-img-top" src="https://static.igem.org/mediawiki/2018/9/99/T--USTC--Model3-Fig13.png" alt=""></a> | <img class="card-img-top" src="https://static.igem.org/mediawiki/2018/9/99/T--USTC--Model3-Fig13.png" alt=""></a> | ||
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| − | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href=" | + | <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="https://2018.igem.org/Team:USTC/Model/Performance_evaluation">Performance evaluation</a></h3> |
<p class="card-text">The goal of our system is to transform nicotine to 3-Succinoyl-Pyrimidine when sensing nicotine. So, it is necessary to evaluate the function and efficiency of the whole system and identify the threshold of sensing system. In simulation, we change the value of promoter strength and nicotine input. From the simulation results, we acquire the best time for sampling, signal measurement and product collection. At last, the recycle rate (production rate) is calculated to prove the efficiency of manufacturing.</p> | <p class="card-text">The goal of our system is to transform nicotine to 3-Succinoyl-Pyrimidine when sensing nicotine. So, it is necessary to evaluate the function and efficiency of the whole system and identify the threshold of sensing system. In simulation, we change the value of promoter strength and nicotine input. From the simulation results, we acquire the best time for sampling, signal measurement and product collection. At last, the recycle rate (production rate) is calculated to prove the efficiency of manufacturing.</p> | ||
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Latest revision as of 03:52, 18 October 2018
Modeling overview
Our project is composed of three systems: sensing system, regulation system and degradation system. Sensing system is used for nicotine detection and regulation system is responsible for expression initiation control. The degradation system synthesizes three kinds of enzymes for nicotine degradation. These three systems are combined together via biological signal molecules AHL. The aim of our modeling is to confirm the feasibility and stability of the whole system in theory. Based on the fundamental model and design, we can choose the appropriate biological parts for expression control according to their parameters. These parts confirmed by modeling can provide us the solution for system optimizing.
Single-cell model
Single-cell model is consisted of 22 nonlinear ordinary differential equations. We use Matlab solvers to simulate the change of species concentration along the time. Then we analyze the condition of initial steady state and nicotine sensing state with defined parameters. These results strongly prove the feasibility of our design.
System analysis
This part we analyze the impact of different combinations of promoter strengths and copy numbers to the initial steady states and outputs with nicotine input. Our simulation results present the tendency of initial state change and the signal shifting. According to these phenomena, we can choose the appropriate promoter combinations and plasmid backbones for the realization of our design.
Performance evaluation
The goal of our system is to transform nicotine to 3-Succinoyl-Pyrimidine when sensing nicotine. So, it is necessary to evaluate the function and efficiency of the whole system and identify the threshold of sensing system. In simulation, we change the value of promoter strength and nicotine input. From the simulation results, we acquire the best time for sampling, signal measurement and product collection. At last, the recycle rate (production rate) is calculated to prove the efficiency of manufacturing.
