Difference between revisions of "Team:BNDS CHINA/Model"

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<h1>Model</h1>
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<h2>I. Summary</h2>
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<p>Our model helped us to optimize the <i>A. hydrophila</i> sensor devices. At first,
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we measured the concentration of C4-HSL in <i>A.
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hydrophila</i> culture by using mass spectrum. Then, we tested the GFP
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production rate of sensor device I. The experimental results were characterized
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by using Hill equation, which modelled the GFP synthesis rate as a function of
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input concentration of the inducer, C4-HSL. However, when we predicted the
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efficiency of this device in real environment by the derived function, we found
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the fluorescence was too low to be detected. Therefore, we adjusted our design
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by increasing rhlR RBS strength and modelled the experimental results by using
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Hill equation again. This time, we found the device’s (BBa_K2548001) fluorescence
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in real environment was enough to be detected. More importantly, our model can
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help to indicate the <i>A. hydrophila</i>
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concentration in different environments, and alerts the aquaculture managers
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the danger of pathogen infection. In addition, we visualize the data in
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three-dimensions to show how GFP production rate per cell over time at different C4-HSL inducer
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concentrations to characterize the sensor in a more comprehensive way. </p>
  
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<h2>II. Assumptions</h2>
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<li> When the concentration of   changes, the synthesis rate of GFP increases, and its fluorescence increases.</li>
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<li> GFP synthesis rate is only affected by the concentration of C4-HSL, and the relationship can be simplified into a non-linear function (Hill equation).</li>
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<li>The function of relevant proteins is assumed stable throughout the experiment. </li>
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<li>The difference between individual subtype of bacteria is omitted.</li>
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</ol>
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<h2>III. Design of Characterizations </h2>
  
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<p>In order to characterize the expression system, we first acquired the experiment data of
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GFP production rate per cell (RFU per Abs per min) under different
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concentration of inducers <img src="/wiki/images/1/12/T--BNDS_CHINA--model002.gif" /> (M) (see experiment HYPERLINK). Then the experimental
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data were fitted using the Hill equation:
  
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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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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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<h3>Best Model Special Prize</h3>
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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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<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 00:26, 16 October 2018

Model

I. Summary

Our model helped us to optimize the A. hydrophila sensor devices. At first, we measured the concentration of C4-HSL in A. hydrophila culture by using mass spectrum. Then, we tested the GFP production rate of sensor device I. The experimental results were characterized by using Hill equation, which modelled the GFP synthesis rate as a function of input concentration of the inducer, C4-HSL. However, when we predicted the efficiency of this device in real environment by the derived function, we found the fluorescence was too low to be detected. Therefore, we adjusted our design by increasing rhlR RBS strength and modelled the experimental results by using Hill equation again. This time, we found the device’s (BBa_K2548001) fluorescence in real environment was enough to be detected. More importantly, our model can help to indicate the A. hydrophila concentration in different environments, and alerts the aquaculture managers the danger of pathogen infection. In addition, we visualize the data in three-dimensions to show how GFP production rate per cell over time at different C4-HSL inducer concentrations to characterize the sensor in a more comprehensive way.

II. Assumptions

  1. When the concentration of   changes, the synthesis rate of GFP increases, and its fluorescence increases.
  2. GFP synthesis rate is only affected by the concentration of C4-HSL, and the relationship can be simplified into a non-linear function (Hill equation).
  3. The function of relevant proteins is assumed stable throughout the experiment.
  4. The difference between individual subtype of bacteria is omitted.

III. Design of Characterizations

In order to characterize the expression system, we first acquired the experiment data of GFP production rate per cell (RFU per Abs per min) under different concentration of inducers (M) (see experiment HYPERLINK). Then the experimental data were fitted using the Hill equation: