Difference between revisions of "Team:SZU-China/Model"
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| − | < | + | <p 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).</p><br /><br /> |
| − | < | + | <p class="font1">The following page shows how we conducted modelling approaches to achieve our goals.</p><br /><br /> |
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<center><span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color: rgb(75, 151, 165); ">Definition</span></center><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 /> | ||
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<center><span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color: rgb(75, 151, 165); ">Procedure</span></center><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 /> | ||
| − | < | + | <p>1. Standardizing variables: transformed variables are of the same scale. Here we utilize the z-score standardizing method. |
| − | </ | + | </p> |
| − | < | + | <p>2. Fitting functions of each variable with polynomial function.</p> |
| − | < | + | <p class="font1">3. Getting the overall relationship using linear least square method.</p><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 /> | <center> <span class="font1" style="font-weight: 500; text-align: center; font-size: 25px; color:rgb(75, 151, 165); ">Results</span></center><br /><br /> | ||
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| − | < | + | <p 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.</p><br /><br /> |
| − | < | + | <p 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.</p><br /><br /> |
| − | <center><span class="font1" style="font-weight:bold;">Overall regression equation:</span></center><br /><br /> | + | <center><span class="font1" style="font-weight:bold;font-size:16px">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> | <div style="text-align:center"><img src="https://static.igem.org/mediawiki/2017/f/f0/T--SZU-China--regression.png" width="60%" ><br /></div> | ||
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| − | < | + | <p 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. </p><br /> |
| − | <br />< | + | <br /><p 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.</p><br /> |
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Revision as of 11:44, 31 October 2017
MODEL
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.
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.
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.
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.
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.
