Difference between revisions of "Team:USTC/Model"

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<h1> Modeling</h1>
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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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<h3> Gold Medal Criterion #3</h3>
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Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.
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The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.
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<h3>Best Model Special Prize</h3>
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                        <h4 class="card-title">Modeling overview</h4>
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                        <p class="card-text">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.</p>
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                                <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="#">Single-cell model</a></h3>
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                                <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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                                <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="#">System analysis</a></h3>
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                                <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>
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                                <h3 class="card-title text-center"><a class="card-link btn btn-primary" style="font-size:1.5rem;" href="#">Effectiveness analysis</a></h3>
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                                <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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To compete for the <a href="https://2018.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2018.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
 
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<h3> Inspiration </h3>
 
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Here are a few examples from previous teams:
 
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<li><a href="https://2016.igem.org/Team:Manchester/Model">2016 Manchester</a></li>
 
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">2016 TU Delft</li>
 
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">2014 ETH Zurich</a></li>
 
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">2014 Waterloo</a></li>
 
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Revision as of 01:43, 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.

Card image cap

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.

Card image cap

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.

Effectiveness analysis

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.