Difference between revisions of "Team:SZU-China/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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To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.
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<h3>Best Model Special Prize</h3>
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To compete for the <a href="https://2017.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://2017.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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<h5> Inspiration </h5>
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<img src="img/modelling-banner.png" width="61.8%">
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Here are a few examples from previous teams:
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<span class="font1">This year our team created a mathematical representation of our concrete self-healing system. This representation, or model, constructs a judging scale in which we can utilize to regulate the four main environmental factors affecting our final concrete healing rate (reflected on mineralization activity). </span><br/><br/>
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<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
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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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<span class="font1">The four factors are: the concentration of spores, carbon sources, nitrogen sources and pH. Based on this model, we can also design the best‘package’ – the vesicle shell with adequate nutrition combination. </span><br/><br/>
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<span class="font1">This modeling process, presented below, can be seen as a feedback between the wet lab (experiment result) and the dry lab(statistic analysis).</span><br/><br/>
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<span class="font1">The following pages show how we conducted modelling approaches to achieve our goals.</span><br/><br/>
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<span class="font1">Our model describes how four main independent variables affect the mineralization activity of our bacillus subtilis.</span><br/><br/>
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<span class="font1">Variables and nomenclature:: dependent variable;: concentration of spores - c[Spore];: concentration of carbon source(C3H5O3Na) - c[C3H5O3Na];: concentration of nitrogen source(NaNO3) - c[NaNO3];: pH of the media - pH
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Goal of model: Use preliminary data to guide future experiment conditions and predict results.</span><br/><br/>
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<span class="font1">Procedure:</span><br/><br/>
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<span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;1.Standardizing variables: transformed variables) are of the same scale. Here we utilize the z-score standardizing method:
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.)</span><br/><br/>
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<span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;2.Fitting functions of each variable with polynomial function.</span><br/><br/>
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<span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;3.Getting the overall relationship using linear least square method.</span><br/><br/>
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<span class="font1">Result:</span><br/><br/>
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<span class="font1">The graph above depicts the polynomial regression of each 4 factors. In general, they each present a tendency for Mineralization activity of rise first and decline later with the growth of each factor.</span><br/><br/>
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<span class="font1">Using the iteration of x into each 4 functions above, then fit x’ with y linealy to get the overall regression equation descrbing each four variables weight.</span><br/><br/>
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<span class="font1">Overall regression equation:</span><br/><br/>
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<img src="img/equ-overall.jpg" width="45%"><br/>
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<span class="font1">From this equation, we can see that nitrogen source has the maximum weight, and pH has the minimum weight, which means nitrogen source are the most essential nutrition for B.subtilis spore. </span><br/>
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<span class="font1">Also, the low weight of pH shows the spore is not sensitive to the change of pH, although there is a sharp decline of activity when pH reaches 11. </span><br/>
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<span class="font1">That is to say, as long as we keep the environment below the boundary high-pH point, it makes no much difference how much we have improved the alkaline resistance of B.subtilis spore. In this way, the modeling instructs us on more appropriate spore micro-environment equipment.</span><br/>
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Revision as of 06:27, 22 October 2017

This year our team created a mathematical representation of our concrete self-healing system. This representation, or model, constructs a judging scale in which we can utilize to regulate the four main environmental factors affecting our final concrete healing rate (reflected on mineralization activity).

The four factors are: the concentration of spores, carbon sources, nitrogen sources and pH. Based on this model, we can also design the best‘package’ – the vesicle shell with adequate nutrition combination.

This modeling process, presented below, can be seen as a feedback between the wet lab (experiment result) and the dry lab(statistic analysis).

The following pages show how we conducted modelling approaches to achieve our goals.

Our model describes how four main independent variables affect the mineralization activity of our bacillus subtilis.

Variables and nomenclature:: dependent variable;: concentration of spores - c[Spore];: concentration of carbon source(C3H5O3Na) - c[C3H5O3Na];: concentration of nitrogen source(NaNO3) - c[NaNO3];: pH of the media - pH Goal of model: Use preliminary data to guide future experiment conditions and predict results.

Procedure:

    1.Standardizing variables: transformed variables) are of the same scale. Here we utilize the z-score standardizing method: .)

    2.Fitting functions of each variable with polynomial function.

    3.Getting the overall relationship using linear least square method.

Result:



The graph above depicts the polynomial regression of each 4 factors. In general, they each present a tendency for Mineralization activity of rise first and decline later with the growth of each factor.

Using the iteration of x into each 4 functions above, then fit x’ with y linealy to get the overall regression equation descrbing each four variables weight.

Overall regression equation:


From this equation, we can see that nitrogen source has the maximum weight, and pH has the minimum weight, which means nitrogen source are the most essential nutrition for B.subtilis spore.
Also, the low weight of pH shows the spore is not sensitive to the change of pH, although there is a sharp decline of activity when pH reaches 11.
That is to say, as long as we keep the environment below the boundary high-pH point, it makes no much difference how much we have improved the alkaline resistance of B.subtilis spore. In this way, the modeling instructs us on more appropriate spore micro-environment equipment.