Difference between revisions of "Team:ShanghaiTech/Model"

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      <div style="z-index: 1; margin-top: -140px;"> <h2>
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        Modeling</h2>
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    <p class="text-center" style="font-weight: bold; font-size: 20pt; margin-top: -50px">Overview</p>
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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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        <p>According to previous reports, we found that designed negative feedback loop (NFBL) system is effective to increase the resetting speed of a circuit and result in higher fidelity of the expression system. Therefore, with the help of modeling, we were able to select a suitable NFBL to meet our project goal.  Meanwhile, we studied the transcriptional layer and used Hill function as the best description for gene transcription. Initially, our data indicated that orthogonal ribosome was efficient to prevent resource competition. Through modeling, we found that orthogonal ribosome had no negative influence on the fidelity of our expression system before and after adding the o-ribosome. After the proof-of-concept, we decided to add the o-ribosome into our expression system. Furthermore, we designed an method to optimize our NFBL circuit, and made a software. Some optimized parameters in our model still need to be tested by experiments in the future. Looking forward, our initial model could be adapted and applied to other projects that aim to fast response and fidelity control.</p>
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          Transcription Layer (Negetive feedback loop)
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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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        <h4>Test idea</h4>
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Please see the <a href="https://2018.igem.org/Judging/Medals"> 2018
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Medals Page</a> for more information.
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        <p>Knowing that negative feedback loop (NFBL) could help to maintain hormones in vivo, we compared NFBL with simple two nodes system. We found that NFBL could fulfilling our goal to build a system with High Fidelity.</p>
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<h3>Best Model Special Prize</h3>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/c/c8/T--ShanghaiTech--8_simple.png"/></p>
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        <p class="text-center"><small>simple system structure</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/a/a9/T--ShanghaiTech--8_NoFeedback.png"/></p>
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        <p class="text-center"><small>simple system result</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/6/6f/T--ShanghaiTech--4_sys3-1.png"/></p>
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        <p class="text-center"><small>NFBL structure</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/1/1b/T--ShanghaiTech--7_NFBL.png"/></p>
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        <p class="text-center"><small>NFBL result</small></p>
  
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        <h4>Build the model describing gene transcription</h4>
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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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You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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        <p>We studied the gene transcription in the loop, and fond that Hill function as the best tool to describe the dynamic reactions in transcription layer.</p>
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        <p>$ rate(downstream) = T\cdot \frac{upstream^{n_{upstream}}}{K_{downstream}+upstream^{n_{upstream}}}$      (Positive influence)</p>
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        <p>$ rate(downstream) = T\cdot \frac{1}{K_{downstream}+upstream^{n_{upstream}}}$      (Negative influence)</p>
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        <h4>Choose the best pattern</h4>
  
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        <p>There are 3 feedback loops available for our system. We compared their properties, and chose system 3-1 to be our circuit template.</p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/8/80/T--ShanghaiTech--10_sys2-1.png"/></p>
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        <p class="text-center"><small>system 2 structure</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/1/1b/T--ShanghaiTech--2_sys2.png"/></p>
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        <p class="text-center"><small>system 2 result</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/6/6f/T--ShanghaiTech--4_sys3-1.png"/></p>
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        <p class="text-center"><small>system 3-1 structure</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/3/37/T--ShanghaiTech--6_sys31.png"/></p>
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        <p class="text-center"><small>system 3-1 result</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/f/f7/T--ShanghaiTech--0_sys3-2.png"/></p>
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        <p class="text-center"><small>system 3-2 structure</small></p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/5/5a/T--ShanghaiTech--8_sys32.png"/></p>
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        <p class="text-center"><small>system 3-2 result</small></p>
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        <h3>Computer experiment</h3>
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        <p>As the experiment went on, we found that it would take too much time that we would not have more time to test many other parts. Then, we turned to model. We built a optimization model to simulate the behavior of the system under different parameters and found the best combination of parameters.</p>
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        <p>Error function $Err = \sum_{i=1}^{n}([SystemOutput]_i-[ExpectOutput]_i)^2$</p>
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        <p><img class="img-fluid mx-auto d-block" style="width: 50%" src="https://static.igem.org/mediawiki/2018/7/74/T--ShanghaiTech--7_search.png"/></p>
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        <p class="text-center"><small>optimum solution search Method</small></p>
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        <h3>Translation Layer (Orthogonal Ribosome)</h3>
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        <p>$$\frac{d[Protein]}{dt} = K_{S16}\cdot[S16]-d_{protein}\cdot[Protein]$$</p>
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        <p>We decided to use orthogonal ribosome to overcome this problem. Before we added orthogonal ribosome to our $\textit{E. coli}$, we used model to illustrate that the orthogonal ribosome would not have negative influence on the fidelity of our system.</p>
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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 01:47, 18 October 2018

ShanghaiTech iGEM

Modeling





Overview


According to previous reports, we found that designed negative feedback loop (NFBL) system is effective to increase the resetting speed of a circuit and result in higher fidelity of the expression system. Therefore, with the help of modeling, we were able to select a suitable NFBL to meet our project goal. Meanwhile, we studied the transcriptional layer and used Hill function as the best description for gene transcription. Initially, our data indicated that orthogonal ribosome was efficient to prevent resource competition. Through modeling, we found that orthogonal ribosome had no negative influence on the fidelity of our expression system before and after adding the o-ribosome. After the proof-of-concept, we decided to add the o-ribosome into our expression system. Furthermore, we designed an method to optimize our NFBL circuit, and made a software. Some optimized parameters in our model still need to be tested by experiments in the future. Looking forward, our initial model could be adapted and applied to other projects that aim to fast response and fidelity control.


Transcription Layer (Negetive feedback loop)


Test idea

Knowing that negative feedback loop (NFBL) could help to maintain hormones in vivo, we compared NFBL with simple two nodes system. We found that NFBL could fulfilling our goal to build a system with High Fidelity.

simple system structure

simple system result

NFBL structure

NFBL result

Build the model describing gene transcription

We studied the gene transcription in the loop, and fond that Hill function as the best tool to describe the dynamic reactions in transcription layer.

$ rate(downstream) = T\cdot \frac{upstream^{n_{upstream}}}{K_{downstream}+upstream^{n_{upstream}}}$ (Positive influence)

$ rate(downstream) = T\cdot \frac{1}{K_{downstream}+upstream^{n_{upstream}}}$ (Negative influence)

Choose the best pattern

There are 3 feedback loops available for our system. We compared their properties, and chose system 3-1 to be our circuit template.

system 2 structure

system 2 result

system 3-1 structure

system 3-1 result

system 3-2 structure

system 3-2 result


Computer experiment


As the experiment went on, we found that it would take too much time that we would not have more time to test many other parts. Then, we turned to model. We built a optimization model to simulate the behavior of the system under different parameters and found the best combination of parameters.

Error function $Err = \sum_{i=1}^{n}([SystemOutput]_i-[ExpectOutput]_i)^2$

optimum solution search Method


Translation Layer (Orthogonal Ribosome)


$$\frac{d[Protein]}{dt} = K_{S16}\cdot[S16]-d_{protein}\cdot[Protein]$$

We decided to use orthogonal ribosome to overcome this problem. Before we added orthogonal ribosome to our $\textit{E. coli}$, we used model to illustrate that the orthogonal ribosome would not have negative influence on the fidelity of our system.


ShanghaiTech iGEM @ 2018