Difference between revisions of "Team:ECNUAS/Model"

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<p>This page is used by the judges to evaluate your team for the <a href="https://2021.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2021.igem.org/Judging/Awards"> award listed below</a>. </p>
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                    <a href="">Project</a>
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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 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. </p>
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<p>Please note you can compete for both the Gold Medal criterion #3 and the Best Model prize with this page. </p>
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Use modeling to gain insight into how your project works or should be implemented. Explain your model's assumptions, data, parameters, and results in a way that anyone could understand.
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<h3>Best Model Special Prize</h3>
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<p>Models and computer simulations provide a great way to describe the functioning and operation of BioBrick Parts and Devices. Synthetic biology is an engineering discipline and part of engineering is simulation and modeling to determine system behavior before building your design. 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 before or in conjunction with experiments in the wetlab.
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To compete for the <a href="https://2021.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://2021.igem.org/Judging/Judging_Form">judging form</a>.
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                                    class="sub-nav-74">Contribution</a></li>
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<p>You can look at what other teams did to get some inspiration! <br />
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Here are a few examples:</p>
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<li><a href="https://2019.igem.org/Team:Exeter/Model">2019 Exeter</li>
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<li><a href="https://2019.igem.org/Team:Mingdao/Model">2019 Mingdao</a></li>
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<li><a href="https://2020.igem.org/Team:Harvard/Model">2020 Harvard</a></li>
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<li><a href="https://2020.igem.org/Team:Leiden/Model">2020 Leiden</a></li>
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                    <a href="https://2021.igem.org/Team:ECNUAS/Implementation">Implementation</a>
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        <div class="sub-title">MODEL</div>
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        <div class="article-content">When we conducted the function tests of our biosensor, we noticed that basically
 +
            there is an increasing trend of the fluorescence intensity of bacteria C as the induction hour increases.
 +
            Therefore, we decided to analyze the quantitative relationship between the fluorescence intensity and the
 +
            detection time by building a model and determine the most appropriate hour for our biosensor to confirm the
 +
            result.</div>
 +
        <div class="article-content">Below is the initial data:</div>
 +
        <div class="img-wrap no-margin">
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            <span>Table 1. The Fluorescence intensity of the bacteria C solution under different concentrations of
 +
                cyanuric acid (CYA) by time</span>
 +
            <img src="https://static.igem.org/mediawiki/2021/a/a7/T--ECNUAS--_model01.jpg" alt="">
 +
        </div>
 +
        <div class="article-content">As each group was conducted with three duplicates in order to minimize the error,
 +
            we used the average to draw scatter plots by MATLAB to further find the fitting function. After several
 +
            attempts, we chose the cubic polynomial equation to adapt to our data and the results are given below where
 +
            all fitting degrees are higher than 0.99, even reaching 1.</div>
 +
        <div class="article-content">The Cubic Polynomial Equation: <i>y = p<sub>1</sub>x<sup>3</sup> +
 +
                p<sub>2</sub>x<sup>2</sup> + p<sub>3</sub>x +
 +
                p<sub>4</sub></i></div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/b/b2/T--ECNUAS--_model03.jpg" alt="">
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            <span>Figure 1. The model result when CYA concentration was given 10 uM/L</span>
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/2/2e/T--ECNUAS--_model04.jpg" alt="">
 +
            <span>Figure 2. The fitting curve of the model when CYA concentration was given 10 uM/L</span>
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/3/33/T--ECNUAS--_model05.jpg" alt="">
 +
            <span>Figure 3. The model result when CYA concentration was given 30 uM/L</span>
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/7/71/T--ECNUAS--_model06.jpg" alt="">
 +
            <span>Figure 4. The fitting curve of the model when CYA concentration was given 30 uM/L</span>
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/a/a6/T--ECNUAS--_model07.jpg" alt="">
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            <span>Figure 5. The model result when CYA concentration was given 50 uM/L</span>
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/4/49/T--ECNUAS--_model08.jpg" alt="">
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            <span>Figure 6. The fitting curve of the model when CYA concentration was given 50 uM/L</span>
 +
        </div>
 +
        <div class="article-title">Comparison</div>
 +
        <div class="article-content">The coding we used to combine all fitting curves in one graph is given below:</div>
 +
        <div class="article-content">
 +
            “<br />
 +
            clear;clc;<br />
 +
            t0=[0 1 2 4 6];<br />
 +
            c10=[549.883333 945.896667 1768.9 4414.20667 6189.83];<br />
 +
            c30=[490.156667 895.046667 1993.16 4270.30333 5789.22333];<br />
 +
            c50=[674.69 1177.77 1953.85667 3809.30667 5341.35333];<br />
 +
            pc10=polyfit(t0,c10,3);<br />
 +
            pc30=polyfit(t0,c30,3);<br />
 +
            pc50=polyfit(t0,c50,3);<br />
 +
            t=[0:0.1:6];<br />
 +
            yc10=polyval(pc10,t);<br />
 +
            yc30=polyval(pc30,t);<br />
 +
            yc50=polyval(pc50,t);<br />
 +
            plot(t,yc10,'r',t,yc30,'b',t,yc50,'g','LineWidth',1)<br />
 +
            x1=0.5;y1=647.6;x2=2.1;y2=2037;x3=2.5;y3=2396;x4=3.3;y4=3472;<br />
 +
            hold on<br />
 +
            plot(x1,y1,'k*');<br />
 +
            plot(x2,y2,'k*');<br />
 +
            plot(x3,y3,'k*');<br />
 +
            plot(x4,y4,'k*');<br />
 +
            hold off<br />
 +
            ”
 +
        </div>
 +
        <div class="img-wrap no-margin">
 +
            <img src="https://static.igem.org/mediawiki/2021/5/51/T--ECNUAS--_model09.jpg" alt="">
 +
            <span>Figure 7. Comparison among three fitting curves</span>
 +
        </div>
 +
        <div class="article-title">Conclusion</div>
 +
        <div class="article-content">
 +
            Based on the model results (Fig. 1, 3, 5), the constants (p1) of the cubic are picked and listed
 +
            below:<br />
 +
            p1 in the model (CYA=10 uM): -44.03<br />
 +
            p1 in the model (CYA=30 uM): -37.33<br />
 +
            p1 in the model (CYA=50 uM): -19.03
 +
        </div>
 +
        <div class="article-content">Hence, we could infer that the less the concentration of CYA is given, the higher
 +
            the changing rate of the curve presents. Especially when the detection hour is given more than 3.3 hours,
 +
            the changing rate of the fitting curve (CYA=10 uM) is obviously higher than the other two, namely, our live
 +
            bacteria biosensor is more sensitive to the lower concentration of CYA solution.</div>
 +
        <div class="article-content">In this case, our biosensor with the live bacteria carrier would be more
 +
            recommended for the low concentration detection of CYA and the detection time is recommended to wait for
 +
            more than 3.3 hours in order to amplify the differences.</div>
 +
        <div class="article-content">In addition, it also indicates that Cell-Free Expression Biosensor is necessary
 +
            which is expected to cover the detection on the higher concentration of CYA.</div>
 +
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Revision as of 16:06, 16 October 2021

ECNUAS

MODEL
When we conducted the function tests of our biosensor, we noticed that basically there is an increasing trend of the fluorescence intensity of bacteria C as the induction hour increases. Therefore, we decided to analyze the quantitative relationship between the fluorescence intensity and the detection time by building a model and determine the most appropriate hour for our biosensor to confirm the result.
Below is the initial data:
Table 1. The Fluorescence intensity of the bacteria C solution under different concentrations of cyanuric acid (CYA) by time
As each group was conducted with three duplicates in order to minimize the error, we used the average to draw scatter plots by MATLAB to further find the fitting function. After several attempts, we chose the cubic polynomial equation to adapt to our data and the results are given below where all fitting degrees are higher than 0.99, even reaching 1.
The Cubic Polynomial Equation: y = p1x3 + p2x2 + p3x + p4
Figure 1. The model result when CYA concentration was given 10 uM/L
Figure 2. The fitting curve of the model when CYA concentration was given 10 uM/L
Figure 3. The model result when CYA concentration was given 30 uM/L
Figure 4. The fitting curve of the model when CYA concentration was given 30 uM/L
Figure 5. The model result when CYA concentration was given 50 uM/L
Figure 6. The fitting curve of the model when CYA concentration was given 50 uM/L
Comparison
The coding we used to combine all fitting curves in one graph is given below:
“
clear;clc;
t0=[0 1 2 4 6];
c10=[549.883333 945.896667 1768.9 4414.20667 6189.83];
c30=[490.156667 895.046667 1993.16 4270.30333 5789.22333];
c50=[674.69 1177.77 1953.85667 3809.30667 5341.35333];
pc10=polyfit(t0,c10,3);
pc30=polyfit(t0,c30,3);
pc50=polyfit(t0,c50,3);
t=[0:0.1:6];
yc10=polyval(pc10,t);
yc30=polyval(pc30,t);
yc50=polyval(pc50,t);
plot(t,yc10,'r',t,yc30,'b',t,yc50,'g','LineWidth',1)
x1=0.5;y1=647.6;x2=2.1;y2=2037;x3=2.5;y3=2396;x4=3.3;y4=3472;
hold on
plot(x1,y1,'k*');
plot(x2,y2,'k*');
plot(x3,y3,'k*');
plot(x4,y4,'k*');
hold off
”
Figure 7. Comparison among three fitting curves
Conclusion
Based on the model results (Fig. 1, 3, 5), the constants (p1) of the cubic are picked and listed below:
p1 in the model (CYA=10 uM): -44.03
p1 in the model (CYA=30 uM): -37.33
p1 in the model (CYA=50 uM): -19.03
Hence, we could infer that the less the concentration of CYA is given, the higher the changing rate of the curve presents. Especially when the detection hour is given more than 3.3 hours, the changing rate of the fitting curve (CYA=10 uM) is obviously higher than the other two, namely, our live bacteria biosensor is more sensitive to the lower concentration of CYA solution.
In this case, our biosensor with the live bacteria carrier would be more recommended for the low concentration detection of CYA and the detection time is recommended to wait for more than 3.3 hours in order to amplify the differences.
In addition, it also indicates that Cell-Free Expression Biosensor is necessary which is expected to cover the detection on the higher concentration of CYA.
Contact Info

WWeChat Official Account: Silent Spring

Email Contact: samlishensheng@qq.com