Difference between revisions of "Team:SZU-China/Model"

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         <section id="overview" style="padding:96px 0 40px 0;background-color:white;">
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                        <h3 class="uppercase color-primary mb40 " style="margin-bottom: 40px;font-size:50px"><center>MODEL</center> </h3>
            <span class="font1">This year, our team creates 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 />
+
            <span class="font1">The four factors are: <br/><img src="https://static.igem.org/mediawiki/2017/c/c6/T--SZU-China--equation.jpg " width="25%"/></br>
+
            <p>Based on this model, we can also design the best‘package'– the vesicle shell with adequate nutrition combination.</p></span><br /><br />
+
            <p></p>
+
            <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 />
+
  
            <span class="font1">The following page shows how we conducted modelling approaches to achieve our goals.To begin with, we make some declaration.</span><br /><br />
 
<br /><br />
 
           
 
                <ul style="font-size:16px">Variables and nomenclature:<li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of spores - c[Spore]</li> <li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of carbon source(C3H5O3Na) - c[C3H5O3Na]</li><li> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of nitrogen source(NaNO3) - c[NaNO3]</li><li>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;pH of the media - pH</li></ul><br/>
 
                <ul style="font-size: 16px">Goal of model<li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use preliminary data to guide future experiment conditions and predict results.</li></ul>
 
            <br /><br />
 
            <center><span class="font1" style="font-weight:bold;text-align:center;font-size:20px">Procedure:</span></center><br /><br />
 
            <span class="font1">
 
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.Standardizing variables: transformed variables are of the same scale. Here we utilize the z-score standardizing method.
 
 
 
            </span><br />
 
            <span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.Fitting functions of each variable with polynomial function.</span><br />
 
            <span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.Getting the overall relationship using linear least square method.</span><br/><br /><br />
 
            <center> <span class="font1" style="font-weight:bold;text-align:center;font-size:20px">Results:</span></center><br /><br />
 
   
 
  
            <div style="text-align: center;margin: 24px 0;z-index:-1;">
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                    </div>
                 <img src="https://static.igem.org/mediawiki/2017/2/24/T--SZU-China--model1.png " width="90%" style="box-shadow: 0px 0px 2px #1E1E1E;">
+
                 </div>
 
             </div>
 
             </div>
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        </section>
  
            <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 />
 
            <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 />
 
            <center><span class="font1" style="font-weight:bold;" >Overall regression equation:</span></center><br /><br />
 
            <div style="text-align:center"><img src="https://static.igem.org/mediawiki/2017/f/f0/T--SZU-China--regression.png" width="60%" height="8%" ><br /></div>
 
  
             <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 is 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. </span><br />
+
        <p></p><br /><br />
            <br/><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 />
+
 
            <span></span><br />
+
        <section style="background-color: rgba(245,245,245,0.45); padding: 96px 0; ">
            <span></span><br />
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             <div style="position: relative; margin: 30px 14%;">
            <span></span><br />
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                <center><span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color: rgb(75, 151, 165); ">Introduction</span></center><br /><br />
<br/><br/><br/><br/><br/><br/>
+
                <span >This year, our team creates a mathematical representation of our concrete self-healing system. This representation, or model, constructs a judging scale with which we can utilize to regulate the four main environmental factors affecting our final concrete healing efficiency (reflected by mineralization activity). </span><br /><br />
        </div>
+
                <p >
 +
                    The four factors are: <br /><img src="https://static.igem.org/mediawiki/2017/c/c6/T--SZU-China--equation.jpg " width="25%" /></br>
 +
                    </p>
 +
                <p>Based on this model, we can also design the best‘package'– the microcapsule with adequate nutrition combination.</p>
 +
                <br />
 +
               
 +
                <span class="font1">This modeling process, presented below, can be seen as a feedback between the wet lab (experiment result) and the dry lab(modelling analysis).</span><br /><br />
 +
 
 +
                <span class="font1">The following page shows how we conducted modelling approaches to achieve our goals.</span><br /><br />
 +
                <br /><br />
 +
                <center><span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color: rgb(75, 151, 165); ">Definition</span></center><br /><br />
 +
                <span>To begin with, we make some definition.</span>
 +
                <br/><br/>
 +
                <ul style="font-size:16px;" class="def">Variables and nomenclature:<li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of spores - c[Spore]</li> <li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of carbon source(C3H5O3Na) - c[C3H5O3Na]</li><li> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;concentration of nitrogen source(NaNO3) - c[NaNO3]</li><li>&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;pH of the media - pH</li></ul><br />
 +
                <ul style="font-size: 16px;" class="def">Goal of model:<li>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use preliminary data to guide future experiments.</li></ul>
 +
                <br /><br />
 +
                <center><span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color: rgb(75, 151, 165); ">Procedure</span></center><br /><br />
 +
                <span >&nbsp;&nbsp;&nbsp;&nbsp;1.Standardizing variables: transformed variables are of the same scale. Here we utilize the z-score standardizing method.
 +
 
 +
                </span><br />
 +
                <span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.Fitting functions of each variable with polynomial function.</span><br />
 +
                <span class="font1">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.Getting the overall relationship using linear least square method.</span><br /><br /><br />
 +
                <center> <span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color:rgb(75, 151, 165); ">Results</span></center><br /><br />
 +
 
 +
 
 +
                <div style="text-align: center;margin: 24px 0;z-index:-1;">
 +
                    <img src="https://static.igem.org/mediawiki/2017/2/24/T--SZU-China--model1.png " width="90%" style="box-shadow: 0px 0px 2px #1E1E1E;">
 +
                </div>
 +
 
 +
                <span class="font1">The graph above depicts the polynomial regression of each 4 factors. In general, for each of those four factors, mineralization activity shows the similar tendency of going up first and down later with the increase of each factor.</span><br /><br />
 +
                <span class="font1">Using the iteration of x into each 4 functions above, then fit x with y linearly to get the overall regression equation describing the respective weight of four variables.</span><br /><br />
 +
                <center><span class="font1" style="font-weight:bold;">Overall regression equation:</span></center><br /><br />
 +
                <div style="text-align:center"><img src="https://static.igem.org/mediawiki/2017/f/f0/T--SZU-China--regression.png" width="60%" ><br /></div>
 +
                <br/>
 +
                <span class="font1">From this equation, we can see that nitrogen source has the maximum weight, while pH has the minimum weight, which means nitrogen source is 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. </span><br />
 +
                <br /><span class="font1">In summary, 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, this model instructs us on more appropriate spore micro-environment equipment.</span><br />
 +
                <span></span><br />
 +
                <span></span><br />
 +
                <span></span><br />
 +
                <br /><br /><br /><br /><br /><br />
 +
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 +
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Revision as of 11:37, 31 October 2017

MODEL



Introduction


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

The four factors are:

Based on this model, we can also design the best‘package'– the microcapsule 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(modelling analysis).

The following page shows how we conducted modelling approaches to achieve our goals.



Definition


To begin with, we make some definition.

    Variables and nomenclature:
  •         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 experiments.


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.


Results


The graph above depicts the polynomial regression of each 4 factors. In general, for each of those four factors, mineralization activity shows the similar tendency of going up first and down later with the increase of each factor.

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

Overall regression equation:




From this equation, we can see that nitrogen source has the maximum weight, while pH has the minimum weight, which means nitrogen source is 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.

In summary, 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, this model instructs us on more appropriate spore micro-environment equipment.