Difference between revisions of "Team:Brasil-USP/Modeling"

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        &nbsp;&nbsp;&nbsp;&nbsp;To have a quantitative understanding of our circuit and test other possibilities, we have developed models to predict gene expression and chemical interactions based on stochastic processes and differential equations. We were able to test our kill switch mechanism, predicting the timing balance in terms of the expression rate of tetR.
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        While developing our models, we kept the following objectives in mind:
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          <li>To analyze our system quantitatively.</li>
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          <li>To gain predictive power.</li>
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          <li>To create framework to scale our system to industrial levels.</li>
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        &nbsp;&nbsp;&nbsp;&nbsp;To make it easier for the reader, we have divided below the main processes.
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<h2> Modeling</h2>
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<h4>Note</h4>
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<p>In order to be considered for the <a href="https://2015.igem.org/Judging/Awards#SpecialPrizes">Best Model award</a>, you must fill out this page.</p>
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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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Here are a few examples from previous teams:
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
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Latest revision as of 20:11, 18 September 2015

Modeling Results

    To have a quantitative understanding of our circuit and test other possibilities, we have developed models to predict gene expression and chemical interactions based on stochastic processes and differential equations. We were able to test our kill switch mechanism, predicting the timing balance in terms of the expression rate of tetR.

While developing our models, we kept the following objectives in mind:

  • To analyze our system quantitatively.
  • To gain predictive power.
  • To create framework to scale our system to industrial levels.

    To make it easier for the reader, we have divided below the main processes.

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