Difference between revisions of "Team:SCUT/Collaborations"

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<h2>Collaboration with Team FAFU-China</h2>
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<h3>Collaboration</h3>
 
<p>
 
<p>
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Sharing and collaboration are core values of iGEM. We encourage you to reach out and work with other teams on difficult problems that you can more easily solve together.
+
Aiming to respond to the requirements of the official, we help the team FAFU-CHINA with some modeling work.<br/>
 
</p>
 
</p>
  
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<h3>The introduction of FAFU-CHINA’s Project</h3>
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<p>
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<h4> Which other teams can we work with? </h4>
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We use the method of RNA interference to suppress the replication of CSBV and to prevent CSBV infecting larvae. The methods of rearing honeybee larvae and viral inoculation under lab conditions were established. CSBV homologous specific dsRNAs (dsRdRP) were synthesized using T7 RiboMAXTM Express RNAi System Kit. The daRNAs and CSBV were added into the food, dsGFP as control. The second instar larvae were first fed with food containing dsRNAs. Twelve hours later, the larvae were then fed with food containing CSBV. The mRNA level of CSBV were detected by RT-qPCR in dsRNAs treated larvae. In order to apply RNA interference technology to production practice RdRP gene cloned into the L4440 vector, and transformed into E.coli strain HT115 to express dsRdRP. Bacteria containing dsRdRP were fed to CSBV-infected colonies for studying its effect on the prevention and treatment of CSBV. <br/>
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<p>  
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You can work with any other team in the competition, including software, hardware, high school and other tracks. You can also work with non-iGEM research groups, but they do not count towards the <a hreef="https://2015.igem.org/Judging/Awards#Medals">iGEM team collaboration gold medal criterion</a>.
+
 
</p>
 
</p>
  
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<h3>Modeling Work</h3>
 
<p>
 
<p>
−
In order to meet the gold medal criteria on helping another team, you must complete this page and detail the nature of your collaboration with another iGEM team.
+
In FAFU’s project, it is difficult to measure the quantitative data and determine the amount of dsRNAs which is fed to the larvae. So in the modeling part, we devoted to establishing an accurate mathematical model to simulate the dsRNA expression according to the mechanism of T7 promoter. After the model is built, we can determine the relationship between the concentration of IPTG and the production of dsRNA . Then we can control  the amount of dsRNAs which if fed to the larvae by controlling the concentration of IPTG easily.<br/>
 +
We know that T7 promoter is a kind of inducible promoter. Hill equation can be used to simulate the effect of T7 promoter. In T7 strength model, the independent variable is the concentration of IPTG, and the dependent variable is the production of dsRNA.<br/>
 
</p>
 
</p>
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</div>
 
  
 +
<h4>1.The Model Simulating the Change of dsRNA with Time in Different Concentration of IPTG</h4>
 
<p>
 
<p>
−
Here are some suggestions for projects you could work on with other teams:
+
By observing the pattern of the data, we figure out that the Logistic equation, which is often used to simulate the growth of population, can model the trend best. Thus, we adapt the formula
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/5/58/2015SCUTCollaboration1.png" alt=" " style="width:300px;height:auto;" />
 +
in the following fitting, where a is the concentration of dsRNA in the steady state.
 +
When the concentration of IPTG=0.3mmol/L, the result of curve fitting is: <br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/5/58/2015SCUTCollaboration1.png" alt=" " style="width:300px;height:auto;" /></p>
 +
<p>
 +
Coefficients (with 95% confidence bounds):<br/>
 +
      a =    0.3241  (0.299, 0.3492)<br/>
 +
      b =    35.95  (-79.99, 151.9)<br/>
 +
      c =    1.829  (0.3231, 3.334)<br/>
 +
Goodness of fit:<br/>
 +
  SSE: 0.00333<br/>
 +
  R-square: 0.9676<br/>
 +
  Adjusted R-square: 0.9567<br/>
 +
  RMSE: 0.02356<br/>
 
</p>
 
</p>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/a/a3/2015SCUTcollaboration-1.png" alt=" "style="width:600px;height:auto;" /></p>
 +
<p style="text-align:center;">
 +
Figure 1. The curve is matched by the formula mentioned above.<br/>
 +
</p>
 +
When the concentration of IPTG=0. 4mmol/L, the result of curve fitting is:<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/5/58/2015SCUTCollaboration1.png" alt=" " style="width:300px;height:auto;" /></p>
 +
Coefficients (with 95% confidence bounds):<br/>
 +
      a =      0.3474  (0.3261, 0.3686)<br/>
 +
      b =      46.66  (-63.55, 156.9)<br/>
 +
      c =      1.828  (0.7647, 2.892)<br/>
 +
Goodness of fit:<br/>
 +
  SSE: 0.00232<br/>
 +
  R-square: 0.9808<br/>
 +
  Adjusted R-square: 0.9744<br/>
 +
  RMSE: 0.01966<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/e/ed/2015SCUTcollaboration-2.png" alt=" "style="width:600px;height:auto;" /></p>
 +
<p style="text-align:center;">
 +
Figure 2. The curve is matched by the formula mentioned above.<br/>
 +
<p>
 +
When the concentration of IPTG=0. 5mmol/L, the result of curve fitting is:<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/5/58/2015SCUTCollaboration1.png" alt=" " style="width:300px;height:auto;" /></p>
 +
Coefficients (with 95% confidence bounds):<br/>
 +
        a =      0.3465  (0.3206, 0.3723)<br/>
 +
      b =      49.63  (-79.79, 179)<br/>
 +
      c =      1.76  (0.6333, 2.886)<br/>
 +
Goodness of fit:<br/>
 +
  SSE: 0.003338<br/>
 +
  R-square: 0.9732<br/>
 +
  Adjusted R-square: 0.9642<br/>
 +
  RMSE: 0.02359<br/>
  
−
<ul>
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<p id="img"><img src="https://static.igem.org/mediawiki/parts/d/d2/2015SCUTcollaboration-3.png" alt=" " style="width:600px;height:auto;" /></p>
−
<li> Improve the function of another team's BioBrick Part or Device</li>
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<p style="text-align:center;">
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<li> Characterize another team's part </li>
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Figure 3.The curve is matched by the formula mentioned above.<br/>
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<li> Debug a construct </li>
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</p>
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<li> Model or simulating another team's system </li>
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−
<li> Test another team's software</li>
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<li> Help build and test another team's hardware project</li>
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<li> Mentor a high-school team</li>
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</ul>
+
  
  
 +
 +
<h4>2.The Hill Equation</h4>
 +
</p>
 +
By the work of first part, it is found that the concentration of dsRNA will become steady after around 4 hours, so we regard the concentration of dsRNA after 4 hours’ culture as that of steady state. Then the Hill equation is applied to model the relationship between the concentration of IPTG and the production of dsRNA, the result of curve fitting is: (where P_max=0.3375 is the maximal data we can get from the data)<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/2/29/2015SCUTCollaboration2.png" alt=" " style="width:300px;height:auto;" /></p>
 +
Coefficients (with 95% confidence bounds):<br/>
 +
      XM =    0.1265  (0.08411, 0.1689)<br/>
 +
      n =      2.239  (0.8187, 3.658)<br/>
 +
Goodness of fit:<br/>
 +
  SSE: 0.003107<br/>
 +
  R-square: 0.9062<br/>
 +
  Adjusted R-square: 0.8828<br/>
 +
  RMSE: 0.02787<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/b/b8/2015SCUTcollaboration-4.png" alt=" " style="width:600px;height:auto;" /></p>
 +
<p style="text-align:center;">
 +
Figure 4.The curve is matched by the formula mentioned above.<br/>
 +
</p>
 +
We sorted out data and parameters and then beautified the graph.<br/>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/a/a1/Collaboration-table.png" alt=" " style="width:400px;height:auto;" /></p>
 +
<p id="img"><img src="https://static.igem.org/mediawiki/parts/4/4f/2015SCUTcollaboration-5.jpg" alt=" " style="width:600px;height:auto;" /></p>
 +
Figure 5. It shows the relationship between the concentration of IPTG and the production of dsRNA.According to this mathematical model, we can work out the accurate production of dsRNA which is fed to the larvae with the concentration of IPTG which is put into the bacterial system to induce T7 promoter.<br/>
 +
</p>
 +
<br/>
 +
 +
The detail can be seen in this page:<br/>
 +
<a href="https://2015.igem.org/Team:FAFU-CHINA/Modeling">https://2015.igem.org/Team:FAFU-CHINA/Modeling</a>
 +
</p>
  
 
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<h3>About Us</h3>
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In 2015, we SCUT teams won top ten innovative and entrepreneurial team set up by SCUT.Because of the strong support of the college, our team is being on the right track, and increasing understanding of the subject and experience.
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<h3 style="margin-left:20px;">Thanks</h3>
 +
<ul>
 +
<li>Zhang Zhenwu,Prof. Guo Shouqian,Dr. Li, Dr. Li Cheng,Dr. Wang Meng,Chen Kejie</li>
 +
<li>Guangzhou Municipal Environmental Protection Bureau<br/>
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Revision as of 21:54, 18 September 2015

Team:SCUT

Collaboration with Team FAFU-China

Collaboration

Aiming to respond to the requirements of the official, we help the team FAFU-CHINA with some modeling work.

The introduction of FAFU-CHINA’s Project

We use the method of RNA interference to suppress the replication of CSBV and to prevent CSBV infecting larvae. The methods of rearing honeybee larvae and viral inoculation under lab conditions were established. CSBV homologous specific dsRNAs (dsRdRP) were synthesized using T7 RiboMAXTM Express RNAi System Kit. The daRNAs and CSBV were added into the food, dsGFP as control. The second instar larvae were first fed with food containing dsRNAs. Twelve hours later, the larvae were then fed with food containing CSBV. The mRNA level of CSBV were detected by RT-qPCR in dsRNAs treated larvae. In order to apply RNA interference technology to production practice RdRP gene cloned into the L4440 vector, and transformed into E.coli strain HT115 to express dsRdRP. Bacteria containing dsRdRP were fed to CSBV-infected colonies for studying its effect on the prevention and treatment of CSBV.

Modeling Work

In FAFU’s project, it is difficult to measure the quantitative data and determine the amount of dsRNAs which is fed to the larvae. So in the modeling part, we devoted to establishing an accurate mathematical model to simulate the dsRNA expression according to the mechanism of T7 promoter. After the model is built, we can determine the relationship between the concentration of IPTG and the production of dsRNA . Then we can control the amount of dsRNAs which if fed to the larvae by controlling the concentration of IPTG easily.
We know that T7 promoter is a kind of inducible promoter. Hill equation can be used to simulate the effect of T7 promoter. In T7 strength model, the independent variable is the concentration of IPTG, and the dependent variable is the production of dsRNA.

1.The Model Simulating the Change of dsRNA with Time in Different Concentration of IPTG

By observing the pattern of the data, we figure out that the Logistic equation, which is often used to simulate the growth of population, can model the trend best. Thus, we adapt the formula

in the following fitting, where a is the concentration of dsRNA in the steady state. When the concentration of IPTG=0.3mmol/L, the result of curve fitting is:

Coefficients (with 95% confidence bounds):
a = 0.3241 (0.299, 0.3492)
b = 35.95 (-79.99, 151.9)
c = 1.829 (0.3231, 3.334)
Goodness of fit:
SSE: 0.00333
R-square: 0.9676
Adjusted R-square: 0.9567
RMSE: 0.02356

Figure 1. The curve is matched by the formula mentioned above.

When the concentration of IPTG=0. 4mmol/L, the result of curve fitting is:

Coefficients (with 95% confidence bounds):
a = 0.3474 (0.3261, 0.3686)
b = 46.66 (-63.55, 156.9)
c = 1.828 (0.7647, 2.892)
Goodness of fit:
SSE: 0.00232
R-square: 0.9808
Adjusted R-square: 0.9744
RMSE: 0.01966

Figure 2. The curve is matched by the formula mentioned above.

When the concentration of IPTG=0. 5mmol/L, the result of curve fitting is:

Coefficients (with 95% confidence bounds):
a = 0.3465 (0.3206, 0.3723)
b = 49.63 (-79.79, 179)
c = 1.76 (0.6333, 2.886)
Goodness of fit:
SSE: 0.003338
R-square: 0.9732
Adjusted R-square: 0.9642
RMSE: 0.02359

Figure 3.The curve is matched by the formula mentioned above.

2.The Hill Equation

By the work of first part, it is found that the concentration of dsRNA will become steady after around 4 hours, so we regard the concentration of dsRNA after 4 hours’ culture as that of steady state. Then the Hill equation is applied to model the relationship between the concentration of IPTG and the production of dsRNA, the result of curve fitting is: (where P_max=0.3375 is the maximal data we can get from the data)

Coefficients (with 95% confidence bounds):
XM = 0.1265 (0.08411, 0.1689)
n = 2.239 (0.8187, 3.658)
Goodness of fit:
SSE: 0.003107
R-square: 0.9062
Adjusted R-square: 0.8828
RMSE: 0.02787

Figure 4.The curve is matched by the formula mentioned above.

We sorted out data and parameters and then beautified the graph.

Figure 5. It shows the relationship between the concentration of IPTG and the production of dsRNA.According to this mathematical model, we can work out the accurate production of dsRNA which is fed to the larvae with the concentration of IPTG which is put into the bacterial system to induce T7 promoter.


The detail can be seen in this page:
https://2015.igem.org/Team:FAFU-CHINA/Modeling

About Us

In 2015, we SCUT teams won top ten innovative and entrepreneurial team set up by SCUT.Because of the strong support of the college, our team is being on the right track, and increasing understanding of the subject and experience.

Thanks

  • Zhang Zhenwu,Prof. Guo Shouqian,Dr. Li, Dr. Li Cheng,Dr. Wang Meng,Chen Kejie
  • Guangzhou Municipal Environmental Protection Bureau

COPYRIGHT ©2015-SCUT