Difference between revisions of "Team:Xiamen City/Model"

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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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<h3> Gold Medal Criterion #3</h3>
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<p>
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        <img src="https://static.igem.org/mediawiki/2021/1/13/T--Xiamen_City--bg_1.jpg" alt="">
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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Please see the <a href="https://2021.igem.org/Judging/Medals">2021 Medals Page</a> for more information.
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            <section class="article p-t-30 p-b-54">
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                <h1 class="content-header">Modeling</h1>
  
<div class="column two_thirds_size">
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                <section>
<h3>Best Model Special Prize</h3>
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                    <p>In order to determine the optimal duration of induction of our engineered yeast, we culture them with
 +
                        β
 +
                        -galactose induced and measured OD<sub>600</sub>. There were two duplicated samples measured in
 +
                        order to eliminate
 +
                        the error and the initial data was given below:</p>
  
<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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                    <div class="img-container">
</p><p>
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                        <span class="figure">Table 1.  OD600 value under β -galactose induction</span>
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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                        <img src="https://static.igem.org/mediawiki/2021/4/41/T--Xiamen_City--ing_model_1.jpg" alt="">
</p>
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                    </div>
  
</div>
+
                    <p>We chose to analyze the relationship between the OD600 value of the engineered yeast and its induced
 +
                        hours. By seeing the scatter-plots, we used the cubic polynomial equation to fit the trends:</p>
  
 +
                    <img src="https://static.igem.org/mediawiki/2021/b/b8/T--Xiamen_City--ing_model_2.jpg" alt="" style="width: 400px;">
 +
                </section>
  
<div class="column third_size">
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                <section>
<div class="highlight decoration_A_full">
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                    <h1 class="title">Coding</h1>
<h3> Inspiration </h3>
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<p>You can look at what other teams did to get some inspiration! <br />
+
Here are a few examples:</p>
+
<ul>
+
<li><a href="https://2018.igem.org/Team:GreatBay_China/Model">2018 GreatBay China</a></li>
+
<li><a href="https://2018.igem.org/Team:Leiden/Model">2018 Leiden</a></li>
+
<li><a href="https://2019.igem.org/Team:IISER_Kolkata/Model">2019 IISER Kolkata</a></li>
+
<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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</ul>
+
</div>
+
</div>
+
  
 +
                    <p>The coding we used to build the models by MATLAB is given below:</p>
 +
 +
                    <pre style="color: #4BACC6">
 +
clear;clc;
 +
t0=[0 2 4 8 24 48 72];%时间
 +
od_1=[0 0.1622 0.2588 0.3451 0.5601 0.9422 0.7602];%样本1数据
 +
od_2=[0 0.1431 0.1703 0.238 0.7319 1.103 0.5699];%样本2数据
 +
p1=polyfit(t0,od_1,3);%拟合样本1多项式系数
 +
p2=polyfit(t0,od_2,3);%拟合样本2多项式系数
 +
t=[0:0.5:72];%每间隔0.5小时取一个点
 +
od600_1=polyval(p1,t);%计算样本1函数值
 +
od600_2=polyval(p2,t);%计算样本2函数值
 +
od_1max=max(od600_1)
 +
od_2max=max(od600_2)
 +
i1=find(od600_1==max(od600_1));
 +
t1=t(i1)
 +
i2=find(od600_2==max(od600_2))
 +
t2=t(i2)
 +
plot(t,od600_1,'b',t,od600_2,'r','LineWidth',2)
 +
hold on
 +
plot(t1,od_1max,'*','LineWidth',2)
 +
plot(t2,od_2max,'*','LineWidth',2)
 +
hold off
 +
            </pre>
 +
                </section>
 +
 +
                <section>
 +
                    <h1 class="title">Model Results</h1>
 +
 +
                    <div class="img-container">
 +
                        <img src="https://static.igem.org/mediawiki/2021/9/9a/T--Xiamen_City--ing_model_3.jpg" alt="">
 +
                        <span class="figure">Figure 1. Model result of Sample 1</span>
 +
                    </div>
 +
 +
                    <div class="img-container">
 +
                        <img src="https://static.igem.org/mediawiki/2021/4/47/T--Xiamen_City--ing_model_4.jpg" alt="">
 +
                        <span class="figure">Figure 2. Fitting curve of the model of Sample 1</span>
 +
                    </div>
 +
 +
                    <div class="img-container">
 +
                        <img src="https://static.igem.org/mediawiki/2021/8/8c/T--Xiamen_City--ing_model_5.jpg" alt="">
 +
                        <span class="figure">Figure 3. Model result of Sample 2</span>
 +
                    </div>
 +
 +
                    <div class="img-container">
 +
                        <img src="https://static.igem.org/mediawiki/2021/7/7f/T--Xiamen_City--ing_model_6.jpg" alt="">
 +
                        <span class="figure">Figure 4. Fitting curve of the model of Sample 2</span>
 +
                    </div>
 +
 +
                    <div class="img-container">
 +
                        <img src="https://static.igem.org/mediawiki/2021/7/75/T--Xiamen_City--ing_model_7.jpg" alt="">
 +
                        <span class="figure">Figure 5. Comparison graph of the fitting curves of sample 1 and sample 2</span>
 +
                    </div>
 +
                </section>
 +
 +
                <section>
 +
                    <h1 class="title">Conclusion </h1>
 +
 +
                    <p>Based on the model results (Fig. 1 & Fig. 3), these models fit the trends well with the fitting
 +
                        degrees
 +
                        higher than 0.96 thus it indicates that these two model equations could be used to predict the
 +
                        optimal
 +
                        induction time of our engineered yeast which would be also served as a reference for future
 +
                        fermentation
 +
                        experiments.</p>
 +
                    <p>In figure 5, we can see that the top points of the two fitting curves fall down to t=48.5 and t=53.
 +
                        Therefore, it indicates that the optimal induction time range would be 48.5~53 hours.</p>
 +
                </section>
 +
            </section>
 +
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 +
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Revision as of 00:17, 13 October 2021

Modeling

In order to determine the optimal duration of induction of our engineered yeast, we culture them with β -galactose induced and measured OD600. There were two duplicated samples measured in order to eliminate the error and the initial data was given below:

Table 1. OD600 value under β -galactose induction

We chose to analyze the relationship between the OD600 value of the engineered yeast and its induced hours. By seeing the scatter-plots, we used the cubic polynomial equation to fit the trends:

Coding

The coding we used to build the models by MATLAB is given below:

clear;clc;
t0=[0 2 4 8 24 48 72];%时间
od_1=[0	0.1622 0.2588 0.3451 0.5601	0.9422 0.7602];%样本1数据
od_2=[0	0.1431 0.1703 0.238	0.7319 1.103 0.5699];%样本2数据
p1=polyfit(t0,od_1,3);%拟合样本1多项式系数
p2=polyfit(t0,od_2,3);%拟合样本2多项式系数
t=[0:0.5:72];%每间隔0.5小时取一个点
od600_1=polyval(p1,t);%计算样本1函数值
od600_2=polyval(p2,t);%计算样本2函数值
od_1max=max(od600_1)
od_2max=max(od600_2)
i1=find(od600_1==max(od600_1));
t1=t(i1)
i2=find(od600_2==max(od600_2))
t2=t(i2)
plot(t,od600_1,'b',t,od600_2,'r','LineWidth',2)
hold on
plot(t1,od_1max,'*','LineWidth',2)
plot(t2,od_2max,'*','LineWidth',2)
hold off
            

Model Results

Figure 1. Model result of Sample 1
Figure 2. Fitting curve of the model of Sample 1
Figure 3. Model result of Sample 2
Figure 4. Fitting curve of the model of Sample 2
Figure 5. Comparison graph of the fitting curves of sample 1 and sample 2

Conclusion

Based on the model results (Fig. 1 & Fig. 3), these models fit the trends well with the fitting degrees higher than 0.96 thus it indicates that these two model equations could be used to predict the optimal induction time of our engineered yeast which would be also served as a reference for future fermentation experiments.

In figure 5, we can see that the top points of the two fitting curves fall down to t=48.5 and t=53. Therefore, it indicates that the optimal induction time range would be 48.5~53 hours.