Difference between revisions of "Team:SSHS-Shenzhen/Model"

 
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{{SSHS-Shenzhen/CSS}}
 
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<head>
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<meta charset="utf-8">
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    <title>Title</title>
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<title>title</title>
 
<style>
 
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padding:150px 50px 5px!important; font-size: 30px!important; text-align: center;
 
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font-size:100px!important;
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width:100%; height:500px;
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text-align:center;
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line-height: 500px;
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padding:90px 0px 0px;
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background-image:url("https://static.igem.org/mediawiki/2018/f/f3/T--SSHS-Shenzhen--jm2.jpg");
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<div class="banner2">
 
<div class="banner2">
−
Model
+
Modeling
 
</div>
 
</div>
 
<h1>
 
<h1>
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</h1>
 
</h1>
 
<p id="para">
 
<p id="para">
−
Our model is formalized by the Differential Equation: (Logistic Regression)</p><br>
+
Our model is formalized by the Linear Equation: (Linear Regression)</p><br>
 
<center>
 
<center>
−
<img src= "https://static.igem.org/mediawiki/2018/a/a1/T--SSHS-Shenzhen--Logistic2.png"
+
<img src= "https://static.igem.org/mediawiki/2018/5/59/T--SSHS-Shenzhen--Dbdmodel11.png"
 
width="20%">
 
width="20%">
 
</center>
 
</center>
 
<p id="para">
 
<p id="para">
−
The Logistic Regression (or the Logistic Model) is a model that is greatly practiced in the field of epidemics. In this case, we chose this model to figure out the relationship between GC content and RNAi efficiency. Instead of guessing a linear function between the two, the Logistic Model helps us to develop a better understanding of this critical parameter which isn't backed by scientific theories.
+
The Linear Regression has wide applications throughout the world of statistics. In this case, we chose this model to figure out the relationship between GC content and RNAi efficiency. By doing so we develop a better understanding of this critical parameter which isn't backed by scientific theories.
 
  </p>
 
  </p>
  
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Parameters
 
Parameters
 
</h1>
 
</h1>
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<br><br>
 
 
<center>
 
<center>
 
<table border="1">
 
<table border="1">
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     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
−
         <td>D(t)</td>
+
         <td>δ</td>
−
         <td>Number of deaths</td>
+
         <td>Deviation of GC content, calculated by the difference between the actual GC content and 50%</td>
 
     </tr>
 
     </tr>
 
  <tr>
 
  <tr>
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     </tr>
 
     </tr>
 
  <tr>
 
  <tr>
−
         <td>α</td>
+
         <td>w</td>
−
         <td>GC content</td>
+
         <td>Correlation, we are expecting a negative value</td>
 +
    </tr>
 +
<tr>
 +
        <td>e</td>
 +
        <td>Error</td>
 
     </tr>
 
     </tr>
 
</table>
 
</table>
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<h1>
 
<h1>
−
Experiments
+
Experiments <a href="https://2018.igem.org/Team:SSHS-Shenzhen/Experiments">(See more)</a>
 
</h1>
 
</h1>
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/7/76/T--SSHS-Shenzhen--Expc1.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Table 1
 +
</b>
 +
shRNAs corresponding to the above siRNAs
 +
</p>
 +
</div>
 +
</center>
 +
<h2>
 +
Day 1 (8 of August, 2018)
 +
</h2>
 
<p id="para">
 
<p id="para">
−
 
+
This is our first bug killing experiment, we prepared our equipment. We put farmed Phyllotreta striolata Fabricius in bottles and make sure each bottle contains 20-25 Phyllotreta striolata Fabricius. We used gauze to cover the top of bottles(gauze can allow bugs to breath but they can’t escape). Then made spray with shRNA (1µL/mL), spray it on the leaves and put the leaves inside the bottles. <br><br>
 +
<b>Summary:</b><br>
 +
1. Put Phyllotreta striolata Fabricius in bottles (20/bottle)<br>
 +
2. Made spray and spray it on the leaves<br>
 +
3. Put leaves into the bottel
 +
</p>
 +
<h2>
 +
Day 2 (10 of August, 2018)
 +
</h2>
 +
<p id="para">
 +
<b>Summary:</b><br>
 +
1. Clean out the leaves from the day before<br>
 +
2. Configure new shRNA spray(1µL/mL)<br>
 +
3. Replace each bottle with 2 leaves soaked in the corresponding reagent<br>
 +
4. Clean out the test bench.
 +
</p>
 +
<h2>
 +
Day 3 (13 of August, 2018)
 +
</h2>
 +
<p id="para">
 +
This is our third bug killing experiment. We configurated new shRNA spray(1µL/mL). Then spray the spray on the new leaves, take out the old leaves and replace them with new leaves. At the same time, we pick out the dead Phyllotreta striolata Fabricius and put them in tubes for RT-PCR testing.<br><br>
 +
<b>Summary:</b><br>
 +
1. Make new spray(1µL/mL). Spray the spray on new leaves<br>
 +
2. Take out the old leaves and replace them with new leaves<br>
 +
3. Pick out the dead Phyllotreta striolata Fabricius and put them in tubes
 +
</p>
 +
<br><br>
 +
<center>
 +
<img src= "https://static.igem.org/mediawiki/2018/5/52/T--SSHS-Shenzhen--Dbdmodel1.png"
 +
width="60%">
 +
</center>
 +
<p id="fig">
 +
Results from day 3
 +
</p>
 +
<h2>
 +
Day 4 (15 of August, 2018)
 +
</h2>
 +
<p id="para">
 +
This is our forth experiment. Like the last experiment, we made new spray and replace the old leaves with new leaves which are sprayed. And then pick out dead Phyllotreta striolata Fabricius and put them in tubes.<br><br>
 +
<b>Summary:</b><br>
 +
1. Make new spray(1µL/mL) and spray it on new leaves<br>
 +
2. Take out the old leaves and replace them with new leaves<br>
 +
3. Pick out the dead Phyllotreta striolata Fabricius and put them in tubes<br>
 +
</p>
 +
<br><br>
 +
<center>
 +
<img src= "https://static.igem.org/mediawiki/2018/2/20/T--SSHS-Shenzhen--Dbdmodel2.png"
 +
width="60%">
 +
</center>
 +
<p id="fig">
 +
Results from day 4
 +
</p>
 +
<h2>
 +
Day 5 (17 of August, 2018)
 +
</h2>
 +
<p id="para"> This is our fifth bug killing experiment, and also is our last bug killing experiment.<br><br>
 +
<b>Summary:</b><br>
 +
1. Take out the dead bugs in the bottle, and take out the leaves in each bottle without putting new leaves<br>
 +
2. Record the number of deaths per bottle of bugs<br>
 +
3. The living bugs stay in the bottle and let them starve to death
 +
</p>
 +
<br><br>
 +
<center>
 +
<img src= "https://static.igem.org/mediawiki/2018/4/4d/T--SSHS-Shenzhen--Dbdmodel3.png"
 +
width="60%">
 +
</center>
 +
<p id="fig">
 +
Results from day 5
 
</p>
 
</p>
−
 
 
<h1>
 
<h1>
 
Results
 
Results
 
</h1>
 
</h1>
−
<br><br>
+
 
 
<center>
 
<center>
−
<img src= "https://static.igem.org/mediawiki/2018/0/04/T--SSHS-Shenzhen--GC.png"
+
<div class="ibox">
−
width="90%">
+
<center><img src="
−
<p id="fig">
+
https://static.igem.org/mediawiki/2018/0/0e/T--SSHS-Shenzhen--Expc2.png
−
Raw data from the experiments
+
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Table 2
 +
</b>
 +
RNAi efficiencies of siRNA/shRNA
 
</p>
 
</p>
 +
</div>
 +
</center>
  
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/d/d0/T--SSHS-Shenzhen--Expb8.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Fig. 1
 +
</b>
 +
The survival rate of Phyllotreta striolata at different days after siRNA/ shRNA treatment.
 +
</p>
 +
</div>
 +
</center>
 +
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/0/02/T--SSHS-Shenzhen--Expb9.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Fig. 2
 +
</b>
 +
Comparison of RNAi efficiencies between siRNA and shRNA
 +
</p>
 +
</div>
 +
</center>
 
<p id="para">
 
<p id="para">
 +
By looking at the statistics generally, we found that our ARK and GLS siRNAs, which has lower deviation has significantly lower survival rates than our ALR siRNAs, which has higher deviation. This matches our expectations.
 +
</p>
  
 +
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/2/2f/T--SSHS-Shenzhen--Dbdmodel8.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Fig. 3
 +
</b>
 +
Sorting out the data for modelling
 
</p>
 
</p>
 +
</div>
 +
</center>
 +
 +
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/9/98/T--SSHS-Shenzhen--modelss2.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Fig. 4
 +
</b>
 +
Survival rates of different samples with variations in time
 +
</p>
 +
</div>
 +
</center>
 +
 
<p id="para">
 
<p id="para">
 +
In Fig. 4, we can see the same trend in the figures before. The deviations of different siRNAs are arranged in increasing order from the left to the right. As time progresses, the difference between our ARK, GLS siRNAs and our ALR siRNAs, becomes more evident.
 +
</p>
  
 +
 +
<center>
 +
<div class="ibox">
 +
<center><img src="
 +
https://static.igem.org/mediawiki/2018/0/0b/T--SSHS-Shenzhen--modelss1.png
 +
" width="100%"></center>
 +
<p id="note">
 +
<b>
 +
Fig. 5
 +
</b>
 +
GC content of different samples versus survival rates with variations in time and trendlines plotted
 +
</p>
 +
</div>
 +
</center>
 +
 +
 +
<p id="para">
 +
In Fig. 5, we plot trendlines to visualize the growing difference. The slopes of our trendlines equal to w, being negative, are increasing in absolute value. Therefore, we know that <b>with a lower deviation of our GC content (from 50%), we get better RNAi efficiency</b>, which is represented by lower survival rates.
 
</p>
 
</p>
 
<br><br><br><br><br>
 
<br><br><br><br><br>
 
</body>
 
</body>
 
</html>
 
</html>

Latest revision as of 10:42, 17 October 2018

Title

title
Modeling

Abstract

Our model is formalized by the Linear Equation: (Linear Regression)


The Linear Regression has wide applications throughout the world of statistics. In this case, we chose this model to figure out the relationship between GC content and RNAi efficiency. By doing so we develop a better understanding of this critical parameter which isn't backed by scientific theories.

Assumptions

The model works under the below assumptions:

1. Births and natural deaths are neglected
2. The beetles cannot recover either by itself or with the help of the leaves
3. Other factors do not change throughout the experiment

Parameters

Parameters Meaning
δ Deviation of GC content, calculated by the difference between the actual GC content and 50%
η RNAi efficiency
w Correlation, we are expecting a negative value
e Error

Experiments (See more)

Table 1 shRNAs corresponding to the above siRNAs

Day 1 (8 of August, 2018)

This is our first bug killing experiment, we prepared our equipment. We put farmed Phyllotreta striolata Fabricius in bottles and make sure each bottle contains 20-25 Phyllotreta striolata Fabricius. We used gauze to cover the top of bottles(gauze can allow bugs to breath but they can’t escape). Then made spray with shRNA (1µL/mL), spray it on the leaves and put the leaves inside the bottles.

Summary:
1. Put Phyllotreta striolata Fabricius in bottles (20/bottle)
2. Made spray and spray it on the leaves
3. Put leaves into the bottel

Day 2 (10 of August, 2018)

Summary:
1. Clean out the leaves from the day before
2. Configure new shRNA spray(1µL/mL)
3. Replace each bottle with 2 leaves soaked in the corresponding reagent
4. Clean out the test bench.

Day 3 (13 of August, 2018)

This is our third bug killing experiment. We configurated new shRNA spray(1µL/mL). Then spray the spray on the new leaves, take out the old leaves and replace them with new leaves. At the same time, we pick out the dead Phyllotreta striolata Fabricius and put them in tubes for RT-PCR testing.

Summary:
1. Make new spray(1µL/mL). Spray the spray on new leaves
2. Take out the old leaves and replace them with new leaves
3. Pick out the dead Phyllotreta striolata Fabricius and put them in tubes



Results from day 3

Day 4 (15 of August, 2018)

This is our forth experiment. Like the last experiment, we made new spray and replace the old leaves with new leaves which are sprayed. And then pick out dead Phyllotreta striolata Fabricius and put them in tubes.

Summary:
1. Make new spray(1µL/mL) and spray it on new leaves
2. Take out the old leaves and replace them with new leaves
3. Pick out the dead Phyllotreta striolata Fabricius and put them in tubes



Results from day 4

Day 5 (17 of August, 2018)

This is our fifth bug killing experiment, and also is our last bug killing experiment.

Summary:
1. Take out the dead bugs in the bottle, and take out the leaves in each bottle without putting new leaves
2. Record the number of deaths per bottle of bugs
3. The living bugs stay in the bottle and let them starve to death



Results from day 5

Results

Table 2 RNAi efficiencies of siRNA/shRNA

Fig. 1 The survival rate of Phyllotreta striolata at different days after siRNA/ shRNA treatment.

Fig. 2 Comparison of RNAi efficiencies between siRNA and shRNA

By looking at the statistics generally, we found that our ARK and GLS siRNAs, which has lower deviation has significantly lower survival rates than our ALR siRNAs, which has higher deviation. This matches our expectations.

Fig. 3 Sorting out the data for modelling

Fig. 4 Survival rates of different samples with variations in time

In Fig. 4, we can see the same trend in the figures before. The deviations of different siRNAs are arranged in increasing order from the left to the right. As time progresses, the difference between our ARK, GLS siRNAs and our ALR siRNAs, becomes more evident.

Fig. 5 GC content of different samples versus survival rates with variations in time and trendlines plotted

In Fig. 5, we plot trendlines to visualize the growing difference. The slopes of our trendlines equal to w, being negative, are increasing in absolute value. Therefore, we know that with a lower deviation of our GC content (from 50%), we get better RNAi efficiency, which is represented by lower survival rates.