Team:Uppsala/Modeling

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<h2>Background</h2>
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<p>This year we decided to create two models for the purpose of further evaluating our project. The first model was developed to show the intended function of our systems and to give a nice visual representation of the cell-cell interaction, see fig 1. This model also gives us insights about flaws and possible improvements of our design, and demonstrate which functions will be most crucial for the efficiency of the Bactissile. Since this model shows individual bacteria and how they interact with each other, we decided to call it the <i>Cell-cell Interaction Model</i>.</p>
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<h1 >WELCOME TO iGEM 2014! </h1>
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<p>Your team has been approved and you are ready to start the iGEM season!
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<br>On this page you can document your project, introduce your team members, document your progress <br> and share your iGEM experience with the rest of the world! </p>
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<p style="color:#E7E7E7"> <a href="https://2014.igem.org/wiki/index.php?title=Team:Uppsala/Modeling&action=edit"style="color:#FFFFFF"> Click here  to edit this page!</a> </p>
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<tr><td><img class="main_pic_left" style="padding-right: 20px;" src="https://static.igem.org/mediawiki/2014/b/b3/Uppsala2014_Mod_screenShot.jpg"></td><td><p><i>Figure 1. A screenshot from an example run of the Cell-Cell Interaction Model</i></p></td></tr>
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<a href="https://2014.igem.org/Team:Uppsala"style="color:#000000">Home </a> </td>
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<a href="https://igem.org/Team.cgi?year=2014&team_name=Uppsala"style="color:#000000"> Official Team Profile </a></td>
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<a href="https://2014.igem.org/Team:Uppsala/Modeling"style="color:#000000"> Modeling</a></td>
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<a href="https://2014.igem.org/Team:Uppsala/Attributions"style="color:#000000"> Attributions </a></td>
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<p>However, the cell-to-cell model lacks in that it does not tell us if our system would be effective enough to actually treat Y.enterocolitica infections in vivo. It can give us a fair estimation, but does not show the big picture. In order to show the big picture we decided to also create a  model displaying large populations of bacteria, to simulate the effect of our construct in the entire infected area. As you might have guessed, this model is therefore called the <i>Population Level Model</i>, since it displays interaction on a population level.  With this model we are able to  variate the different parameters that can be optimized, such as production rates and thresholds, and then observe the effects of the treatment.</p>
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<tr><td colspan="3"> <h3>Modeling</h3></td></tr>
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<p>If you choose to create a model during your project, please write about it here. Modeling is not an essential part of iGEM, but we encourage any and all teams to model some aspect of their project. See previous "Best Model" awards for more information.</p>
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Latest revision as of 19:15, 17 October 2014

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Background

This year we decided to create two models for the purpose of further evaluating our project. The first model was developed to show the intended function of our systems and to give a nice visual representation of the cell-cell interaction, see fig 1. This model also gives us insights about flaws and possible improvements of our design, and demonstrate which functions will be most crucial for the efficiency of the Bactissile. Since this model shows individual bacteria and how they interact with each other, we decided to call it the Cell-cell Interaction Model.


Figure 1. A screenshot from an example run of the Cell-Cell Interaction Model


However, the cell-to-cell model lacks in that it does not tell us if our system would be effective enough to actually treat Y.enterocolitica infections in vivo. It can give us a fair estimation, but does not show the big picture. In order to show the big picture we decided to also create a model displaying large populations of bacteria, to simulate the effect of our construct in the entire infected area. As you might have guessed, this model is therefore called the Population Level Model, since it displays interaction on a population level. With this model we are able to variate the different parameters that can be optimized, such as production rates and thresholds, and then observe the effects of the treatment.