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<div style="position:fixed; top: 80%; left: 90%;"><a href="#top"><img src="https://static.igem.org/mediawiki/2017/3/31/TsinghuaA-backtotop.png" width="80" height="80"></a></div>
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{{Tsinghua-A}}
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<div class="myTitle1" id="top" >Fluid model</div>
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<div class="myTitle2" >I Introduction</div>
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<div class="myPara" > &nbsp;&nbsp;&nbsp;&nbsp;The system we designed involves many kinds of relationships between populations and individuals. To help us understand these relationships better, we designed models to describe how the amount of E.coli and substance change with time.<br>
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&nbsp;&nbsp;&nbsp;&nbsp;Concretely, to consider population interactions in a fluid environment, we designed a fluid model based on ODE. The system we considered here has no substance exchange except for some gas, which is, however, not considered in our model. <br>
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<title>Discription</title>
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&nbsp;&nbsp;&nbsp;&nbsp;What's more, we can use this model to help us design our experiment. (See more details at <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_model/improved_gene_circuit"><font style="font-style:italic;">Improved gene circuit</font></a> and <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_model/regulation_of_killing_ability"><font style="font-style:italic;">Regulation of killing ability</font></a>) <br>
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&nbsp;&nbsp;&nbsp;&nbsp;Furthermore, we found that it will be much easier to consider the effects of some factors (Like the initial number of each populations) on population interactions if we can visualize it. Therefore, we designed our game---Fluid E.coli War based on our fluid model to satisfy our needs. What's more, the game can also help us realize educational purpose of our project and make public know more about synthetic biology! (<a href="https://2017.igem.org/Team:Tsinghua-A/Engagement">Exhibition at National Museum</a>) <br>
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<meta name="description" content="Free HTML5 Template by FREEHTML5.CO" />
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&nbsp;&nbsp;&nbsp;&nbsp;Our game is made on the structure of html5. The small video below is Fluid E.coli War.
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<div class="myPic1" ><center><video src="https://static.igem.org/mediawiki/2017/f/f8/TsinghuaA-Fluidgameplay.MP4" width="400" height="300" controls="controls" style=" text-align:center"></video></center></div>
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<div class="myPara" >&nbsp;&nbsp;&nbsp;&nbsp;See more details on <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_game"><font style="font-style:italic;">Fluid E.coli War</font></a>.<br><br></div>
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<div class="myTitle2" >II Gene circuit of six characters</div>
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<div class="myPic1" ><img src="https://static.igem.org/mediawiki/2017/b/b7/Gene_circuit1.png" width="810.67" height="587"></div>
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<div class="myPara" ><strong>Note: Here for simplicity, we hypothesize that this gene circuit is orthogonal.</strong><br><br></div>
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<div class="myTitle2" >III ODE, explanation and parameter resource</div>
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<div class="myPara" >Note: <br>
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1. Subscript 1 indicate warrior I, subscript 2 indicate farmer I, subscript 3 indicate beggar I, subscript 4 indicate warrior I, subscript 5 indicate farmer II, subscript 6 indicate beggar II.<br>
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2. Volume of medium (Vsys) here is 0.005L. Volume of E.coli(Vcell) is 1μm<sup>3</sup><br><br>
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Detailed ODEs are shown below:<br>
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<strong>1. Amount of population (cell/medium):</strong>
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<div class="myPic2" ><img src="https://static.igem.org/mediawiki/2017/6/60/Tsinghua-a-f_1.jpg"width="700px"></div>
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<div class="myPic2" ><img src="https://static.igem.org/mediawiki/2017/f/ff/Tsinghua-a-f_1_2.jpg"width="700px"></div>
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<div class="myPic2" ><img src="https://static.igem.org/mediawiki/2017/7/78/Tsinghua-a-f_2.jpg"width="700px"></div>
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<div class="myPara" ><strong>2. Concentration of sucrose inside medium：S(sucrose/medium)1 medium=0.005L</strong><br></div>
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<div class="myPara" ><strong>3. Concentration of invertase inside medium：Inv(invertase/medium)</strong><br></div>
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<div class="myPic2" ><img src="https://static.igem.org/mediawiki/2017/0/0c/Tsinghua-a-f_4.jpg"width="700px"></div>
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<div class="myPara" ><strong>4. Concentration of glucose + fructose inside medium: glu.(glucose/medium) 1 medium=0.005L</strong><br></div>
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<div class="myPara" ><strong>5. Carrying capacity：K<sub>n</sub></strong><br></div>
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<div class="myPara" ><strong>6. Concentration of 3OC6HSL molecule inside cell：C<sub>6n</sub>. (C<sub>6</sub>/cell)</strong><br></div>
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<div class="myPara" ><strong>7. Concentration of 3OC12HSL molecule inside cell：C<sub>12n</sub>. (C<sub>12</sub>/cell)</strong><br></div>
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<div class="myPara" ><strong>8. Concentration of 3OC6HSL molecule inside medium: C<sub>6e</sub> (C<sub>6</sub>/L)</strong><br></div>
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<div class="myPara" ><strong>9. Concentration of 3OC12HSLinside medium C<sub>12e</sub>(C<sub>12</sub>/L)</strong><br></div>
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<div class="myPara" ><strong>10. Concentration of lacI inside cell：<i>lacI</i><sub>n</sub>.<i>lacI</i>/cell</strong><br></div>
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<div class="myPara" ><strong>11.Concentration of CmR inside cell：<i>CmR</i><sub>n</sub>. <i>CmR</i>/cell</strong><br></div>
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<div class="myPara" ><strong>12.Concentration of LuxI inside warrior I. LuxI (LuxI/cell)</strong><br></div>
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<div class="myPara" ><strong>13.Concentration of LasI inside warrior II. LasI (LasI/cell)</strong><br></div>
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<div class="myPara" ><strong>14. Concentration of chloramphenicol inside cell: <i>Cm</i>. (<i>Cm</i>/cell)</strong><br></div>
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<div class="myPara" ><strong>15.Concentration of chloramphenicol inside medium:Cm<sub>e</sub> (Cm/L)</strong><br></div>
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<div class="myTitle2" >IV Results</div>
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<div class="myPara" >&nbsp;&nbsp;&nbsp;&nbsp;We extract part of this complete model and adapt it to our experiment results so that we can know how to design our experiment.<br>
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Concretely, we use our model to help us do things below:<br>
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(1)Design warriors that can be killed only by the warrior from the other group. See more at <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_model/improved_gene_circuit">Improved gene circuit</a>.<br>
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(2)Design an easy way to regulate killing ability of warriors. See more at <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_model/regulation_of_killing_ability">Regulation of killing abilities</a>.<br>
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&nbsp;&nbsp;&nbsp;&nbsp;What is more, we use this model to design a game---Fluid E.coli War to help us consider effects of some factors (Like initial number of each populations) on population interactions and make the public more interested in synthetic biology. (See more at <a href="https://2017.igem.org/Team:Tsinghua-A/fluid_game">Fluid E.coli War</a>)<br>
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<div class="myTitle2" >V Discussion</div>
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<div class="myPara" ><strong>Why we detect RFP intensity but not OD600 directly in simplified model used in experiment</strong><br>
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&nbsp;&nbsp;&nbsp;&nbsp;When we look at the model, we will find most part of the model uses hypothesis that is commonly used, like Volterra-Lotka equation or Michaelis-Menten equation, and they can be confirmed experimentally. However,some parts of it are not, like the hypothesis that the decreasing effect of chloramphenicol on population growth rate is proportional to the amount of chloramphenicol inside that character. This is not confirmed by our experiment yet. Therefore, it is still not reasonable now to detect OD600 directly in simplified model used in experiment design. What's more, using RFP intensity to detect LacI and thus killing effect is confirmed by our experiment---Killing Test and so we detect RFP intensity to indicate killing effect. <br><br>
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              <div class="myTitle1">Fluid <i>E.coli War</i></div>
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<div class="myTitle2" >VI Reference</div>
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<div class="myPara" >[1] Wolfenden R, Yuan Y. Rates of spontaneous cleavage of glucose, fructose, sucrose, and trehalose in water, and the catalytic proficiencies of invertase and trehalas[J]. Journal of the American Chemical Society, 2008, 130(24):7548.<br>
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[2] Yadira B, Alejandro V, Jesus, P. Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit[DB/OL]. BioRxiv, 2017(2017-2-6)[2017-6-1]. https://www.biorxiv.org/content/early/2017/02/06/106229. DOI:10.1101/106229.<br>
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[3] BioNumbers. [2017-6-1]. http://www.bionumbers.hms.harvard.edu/<br>
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                        <th colspan="3">Init</th>
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[4] Chen Y, Kim J K, Hirning A J, et al. SYNTHETIC BIOLOGY. Emergent genetic oscillations in a synthetic microbial consortium[J]. Science, 2015, 349(6251):986-9.<br>
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                        <th rowspan="2">Final</th>
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[5] Pai A, You L. Optimal tuning of bacterial sensing potential[J]. Molecular Systems Biology, 2009, 5(1):286-286.<br>
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                        <th rowspan="2">Result</th>
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[6] ETH_Zurich 2014: https://2014.igem.org/Team:ETH_Zurich/modeling/whole#Alternate_Design<br>
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[7] Murray I A, Shaw W V. O-Acetyltransferases for chloramphenicol and other natural products.[J]. Antimicrobial Agents & Chemotherapy, 1997, 41(1):1.<br>
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<div class="myPara" ><br><br></div>
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<div class="myPicDis"><a href="https://2017.igem.org/Team:Tsinghua-A">Home</a>&nbsp;&nbsp;&nbsp;&nbsp;Copyright©&nbsp;iGEM&nbsp;2017&nbsp;Tsinghua-A.&nbsp;All&nbsp;rights&nbsp;reserved.&nbsp;&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;&nbsp;Follow&nbsp;us:&nbsp;igem2017THU_A@163.com</div>
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    <div class="myTitle2">I Introdution</div>
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;The game “E.coli War” is designed based on our liquid model. In the game, E.coli are divided into two groups, with each group having three kinds of E.coli: warriors, farmers and beggars.</div>
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<div class="myPS">PS：The nutrients produced by farmers can be utilized by all E.colis. It can simulate grabbing of the enemy's grain during a war.<br><br></div>
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            <div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;There are 60,000 E.colis at the beginning of a game. They are divided equally into farmers,warriors and beggars. You and AI will get a total of 30,000 E.coli, but the distribution of each kind of E.coli is random. The game is composed of five rounds. In each round, you can allocate 6,000 E.coli .The maximal number of E.coli that your group eventually reach will be your grade. If your grade is greater than that of AI, then you win this round. If you win three rounds, then congratulations, you win the game! <br>We believe you are smart enough to allocate your E.coli reasonably and win the game!</div>
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;OK, Let’s Begin to learn how to play it!<br>&nbsp;&nbsp;&nbsp;&nbsp;Before each game begins, You can input the number of each bacteria.</div>
 
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;Then you can press “Start” to begin this game.</div>
 
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;Now, they begin to kill each other, and the number of them is displayed at top.(The numbers are showed logarithmically. For example, when the length is 10, the number of bacteria is 10^10. If you do not want to wait for too long, you can press "Fast" to accelerate the process)<br>&nbsp;&nbsp;&nbsp;&nbsp;After each round, you can click the button "OK" to play the next round. Or you can click the button "Quit" to exit the game.</div>
 
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;Although we cannot figure out exactly how initialized numbers determine the result, we do find out some rules. Here are some of them and hope that they can help you to win. <br>&nbsp;&nbsp;&nbsp;&nbsp;Because farmers provide nutrients for both sides, they have no effect in each side in terms of nutrients. Farmers only play a role in counting the total number of bacteria. Take this into consideration when you are placing farmers. <br>&nbsp;&nbsp;&nbsp;&nbsp;Warriors can attack enemy, which is beneficial for their own side. Beggars consume their own resources, so they are harmful to their own side. When the number of our warriors is bigger than AI's, and the number of our beggars is less than AI's, we will be more likely to win. The mechanism of winning is that our bacteria will keep growing faster.</div>
 
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<div class="myPara">&nbsp;&nbsp;&nbsp;&nbsp;Because beggars do not have any production, they grow the fastest. When the number of warriors of both sides is closer and the number of our beggars is bigger than AI's, we will be more likely to win. The mechanism of winning is that the number of our beggars can be far greater than AI's.</div>
 
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Latest revision as of 15:55, 1 November 2017

HOME
TEAM
PROJECT
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JUDGING FORM
Discription
Fluid model
I Introduction
    The system we designed involves many kinds of relationships between populations and individuals. To help us understand these relationships better, we designed models to describe how the amount of E.coli and substance change with time.
    Concretely, to consider population interactions in a fluid environment, we designed a fluid model based on ODE. The system we considered here has no substance exchange except for some gas, which is, however, not considered in our model.
    What's more, we can use this model to help us design our experiment. (See more details at Improved gene circuit and Regulation of killing ability)
    Furthermore, we found that it will be much easier to consider the effects of some factors (Like the initial number of each populations) on population interactions if we can visualize it. Therefore, we designed our game---Fluid E.coli War based on our fluid model to satisfy our needs. What's more, the game can also help us realize educational purpose of our project and make public know more about synthetic biology! (Exhibition at National Museum)
    Our game is made on the structure of html5. The small video below is Fluid E.coli War.
    See more details on Fluid E.coli War.

II Gene circuit of six characters
Note: Here for simplicity, we hypothesize that this gene circuit is orthogonal.

III ODE, explanation and parameter resource
Note:
1. Subscript 1 indicate warrior I, subscript 2 indicate farmer I, subscript 3 indicate beggar I, subscript 4 indicate warrior I, subscript 5 indicate farmer II, subscript 6 indicate beggar II.
2. Volume of medium (Vsys) here is 0.005L. Volume of E.coli(Vcell) is 1μm3

Detailed ODEs are shown below:
1. Amount of population (cell/medium):
2. Concentration of sucrose inside medium：S(sucrose/medium)1 medium=0.005L
3. Concentration of invertase inside medium：Inv(invertase/medium)
4. Concentration of glucose + fructose inside medium: glu.(glucose/medium) 1 medium=0.005L
5. Carrying capacity：Kn
6. Concentration of 3OC6HSL molecule inside cell：C6n. (C6/cell)
7. Concentration of 3OC12HSL molecule inside cell：C12n. (C12/cell)
8. Concentration of 3OC6HSL molecule inside medium: C6e (C6/L)
9. Concentration of 3OC12HSLinside medium C12e(C12/L)
10. Concentration of lacI inside cell：lacIn.lacI/cell
11.Concentration of CmR inside cell：CmRn. CmR/cell
12.Concentration of LuxI inside warrior I. LuxI (LuxI/cell)
13.Concentration of LasI inside warrior II. LasI (LasI/cell)
14. Concentration of chloramphenicol inside cell: Cm. (Cm/cell)
15.Concentration of chloramphenicol inside medium:Cme (Cm/L)

IV Results
    We extract part of this complete model and adapt it to our experiment results so that we can know how to design our experiment.
Concretely, we use our model to help us do things below:
(1)Design warriors that can be killed only by the warrior from the other group. See more at Improved gene circuit.
(2)Design an easy way to regulate killing ability of warriors. See more at Regulation of killing abilities.
    What is more, we use this model to design a game---Fluid E.coli War to help us consider effects of some factors (Like initial number of each populations) on population interactions and make the public more interested in synthetic biology. (See more at Fluid E.coli War)

V Discussion
Why we detect RFP intensity but not OD600 directly in simplified model used in experiment
    When we look at the model, we will find most part of the model uses hypothesis that is commonly used, like Volterra-Lotka equation or Michaelis-Menten equation, and they can be confirmed experimentally. However,some parts of it are not, like the hypothesis that the decreasing effect of chloramphenicol on population growth rate is proportional to the amount of chloramphenicol inside that character. This is not confirmed by our experiment yet. Therefore, it is still not reasonable now to detect OD600 directly in simplified model used in experiment design. What's more, using RFP intensity to detect LacI and thus killing effect is confirmed by our experiment---Killing Test and so we detect RFP intensity to indicate killing effect.

VI Reference
[1] Wolfenden R, Yuan Y. Rates of spontaneous cleavage of glucose, fructose, sucrose, and trehalose in water, and the catalytic proficiencies of invertase and trehalas[J]. Journal of the American Chemical Society, 2008, 130(24):7548.
[2] Yadira B, Alejandro V, Jesus, P. Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit[DB/OL]. BioRxiv, 2017(2017-2-6)[2017-6-1]. https://www.biorxiv.org/content/early/2017/02/06/106229. DOI:10.1101/106229.
[3] BioNumbers. [2017-6-1]. http://www.bionumbers.hms.harvard.edu/
[4] Chen Y, Kim J K, Hirning A J, et al. SYNTHETIC BIOLOGY. Emergent genetic oscillations in a synthetic microbial consortium[J]. Science, 2015, 349(6251):986-9.
[5] Pai A, You L. Optimal tuning of bacterial sensing potential[J]. Molecular Systems Biology, 2009, 5(1):286-286.
[6] ETH_Zurich 2014: https://2014.igem.org/Team:ETH_Zurich/modeling/whole#Alternate_Design
[7] Murray I A, Shaw W V. O-Acetyltransferases for chloramphenicol and other natural products.[J]. Antimicrobial Agents & Chemotherapy, 1997, 41(1):1.


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