Difference between revisions of "Team:FAFU-CHINA/Meeting"

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<div id="headline">Meeting</div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>The team has a meeting once a week. We report progresses of our project and discusses about the assignment what we will do next week. Our teammates are passionate for discussions and everyone shares documents they have read and what they have learned. Teammates ask questions about the papers shared. It usually take a long time to discuss questions related to our iGEM project. This activity drives us to read more literature and learn more knowledge.</p></div>
<center><div>Meeting</div></center>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>The advisers not only attend our meetings to listen our reports about experiment progresses, but also provide suggestions on the project. They may share research trends in their lab as well. What’s more, sometimes they give a talk to introduce some experimentation which really light up our experiments.</p></div><br>
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<div>The team has a meeting once a week. We report progresses of our project and discusses about the assignment what we will do next week. Our teammates are passionate for discussions and everyone shares documents they have read and what they have learned. Teammates ask questions about the papers shared. It usually take a long time to discuss questions related to our iGEM project. This activity drives us to read more literature and learn more knowledge.</div>
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<div id="sub"><p>
<div>The advisers not only attend our meetings to listen our reports about experiment progresses, but also provide suggestions on the project. They may share research trends in their lab as well. What’s more, sometimes they give a talk to introduce some experimentation which really light up our experiments.</div>
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    <img src="https://static.igem.org/mediawiki/2015/b/b8/FAFU-CHINA_MT3.jpg"/>
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    <img src="https://static.igem.org/mediawiki/2015/b/b8/FAFU-CHINA_MT2.jpg"/>
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    <img src="https://static.igem.org/mediawiki/2015/b/b8/FAFU-CHINA_MT1.jpg"/>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>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.</p></div>
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<div id="sub"><div id="headline2">1.The Model Simulating the Change of dsRNA with Time in Different Concentration of IPTG</div></div>
 
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<img src='https://static.igem.org/mediawiki/2015/b/b8/FAFU-CHINA_MT3.jpg'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>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<img src="https://static.igem.org/mediawiki/2015/6/67/FAFU-CHINA_MD1.png" height="53" width="178" />in the following fitting, where a is the concentration of dsRNA in the steady state.</p></div><br>
<img src='https://static.igem.org/mediawiki/2015/f/f9/FAFU-CHINA_MT2.jpg'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
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<img src="https://static.igem.org/mediawiki/2015/f/fc/FAFU-CHINA_MT1.jpg">
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<div id="sub"><p>When the concentration of IPTG=0.3mmol/L, the result of curve fitting is: </p></div>
  
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>General model: <img src="https://static.igem.org/mediawiki/2015/6/67/FAFU-CHINA_MD1.png" height="53" width="178" /></p></div>
  
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Coefficients (with 95% confidence bounds): </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>a = 0.3241 (0.299, 0.3492)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>b = 35.95 (-79.99, 151.9)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>c = 1.829 (0.3231, 3.334)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Goodness of fit: </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>SSE: 0.00333</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>R-square: 0.9676</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>Adjusted R-square: 0.9567</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>RMSE: 0.02356</p></div>
  
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/2/2c/FAFU-CHINA_MD2.png" height="393.3" width="608.4"/></p></div>
  
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<div id="sub"><i><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 1: The curve is matched by the formula mentioned above.</i></div>
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<div id="sub"><p>When the concentration of IPTG=0.4mmol/L, the result of curve fitting is: </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>General model: <img src="https://static.igem.org/mediawiki/2015/6/67/FAFU-CHINA_MD1.png" height="53" width="178" /></p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Coefficients (with 95% confidence bounds): </p></div>
<h1 class="animate scaleIn">A5源码</h1>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>a = 0.3474 (0.3261, 0.3686)</p></div>
<img class="animate scaleIn delay-3" src="world.svg" alt="" />
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>b = 46.66 (-63.55, 156.9)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>c = 1.828 (0.7647, 2.892)</p></div>
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<h1 class="animate slideInLeft ">Communication Design</h1>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Goodness of fit: </p></div>
<p class="animate slideInLeft delay-3">Communication design is a mixed discipline between design and information-development which is concerned with how media intermission such as printed, crafted, electronic media or presentations communicate with people.</p>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>SSE: 0.00232</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>R-square: 0.9808</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>Adjusted R-square: 0.9744</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>RMSE: 0.01966</p></div>
<h1 class="animate slideInLeft">Communication Design</h1>
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<p class="animate slideInLeft delay-2">Communication design is a mixed discipline between design and information-development which is concerned with how media intermission such as printed, crafted, electronic media or presentations communicate with people.</p>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/e/ea/FAFU-CHINA_MD3.png" height="393.3" width="608.4"/></p></div>
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<div id="sub"><i><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 2: The curve is matched by the formula mentioned above.</i></div>
<img class="animate slideInRight delay-3" src="world.svg" alt="" />
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<div id="sub"><p>When the concentration of IPTG=0.5mmol/L, the result of curve fitting is: </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>General model: <img src="https://static.igem.org/mediawiki/2015/6/67/FAFU-CHINA_MD1.png" height="53" width="178" /></p></div>
<h1 class="animate scaleIn">Communication Design</h1>
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<img class="animate scaleIn delay-3" src="world.svg" alt="" />
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Coefficients (with 95% confidence bounds): </p></div>
</section>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>a = 0.3465 (0.3206, 0.3723)</p></div>
<section class="com__section com__section--text">
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>b = 49.63 (-79.79, 179)</p></div>
<h1 class="animate slideInLeft ">Communication Design</h1>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>c = 1.76 (0.6333, 2.886)</p></div>
<p class="animate slideInLeft delay-3">Communication design is a mixed discipline between design and information-development which is concerned with how media intermission such as printed, crafted, electronic media or presentations communicate with people.</p>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Goodness of fit: </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>SSE: 0.003338</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>R-square: 0.9732</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>Adjusted R-square: 0.9642</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>RMSE: 0.02359</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/e/e6/FAFU-CHINA_MD4.png" height="393.3" width="608.4"/></p></div>
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<div id="sub"><i><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 3: The curve is matched by the formula mentioned above.</i></div>
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<div id="sub"><div id="headline2">2.The Hill Equation</div></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>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  is the maximal data we can get from the data)</p></div><br>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>General model: <img src="https://static.igem.org/mediawiki/2015/f/f2/FAFU-CHINA_MD5.png" height="53" width="178" /></p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Coefficients (with 95% confidence bounds): </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>Xm = 0.1265 (0.08411, 0.1689)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>n = 2.239 (0.8187, 3.658)</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Goodness of fit: </p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>SSE: 0.003107</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>R-square: 0.9062</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>Adjusted R-square: 0.8828</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:50px;"></span>RMSE: 0.02787</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/7/73/FAFU-CHINA_MD6.png" height="393.3" width="608.4"/></p></div>
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<div id="sub"><i><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 4: The curve is matched by the formula mentioned above.</i></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>We sorted out data and parameters and then beautified the graph.</p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/d/d6/FAFU-CHINA_MD7.png" width="608"/></p></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span><img src="https://static.igem.org/mediawiki/2015/e/ea/FAFU-CHINA_MD8.png" height="393.3" width="608.4"/></p></div>
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<div id="sub"><i><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 5: It shows the relationship between the concentration of IPTG and the production of dsRNA.</i></div>
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<div id="sub"><p><span style="display:-moz-inline-box; display:inline-block; width:25px;"></span>Figure 5 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.</p></div>
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Revision as of 20:48, 18 September 2015

Modeling





Meeting


The team has a meeting once a week. We report progresses of our project and discusses about the assignment what we will do next week. Our teammates are passionate for discussions and everyone shares documents they have read and what they have learned. Teammates ask questions about the papers shared. It usually take a long time to discuss questions related to our iGEM project. This activity drives us to read more literature and learn more knowledge.

The advisers not only attend our meetings to listen our reports about experiment progresses, but also provide suggestions on the project. They may share research trends in their lab as well. What’s more, sometimes they give a talk to introduce some experimentation which really light up our experiments.


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 formulain 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:

General model:

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:

General model:

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:

General model:

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 is the maximal data we can get from the data)


General model:

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.

Figure 5 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.