Difference between revisions of "Team:Aalto-Helsinki/Modeling"

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{{Aalto-Helsinki/CSS}}
 
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#modelingnav:visited {
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<div class="compscreens">
 
<div class="inner-container">
 
<div class="inner-container">
  
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<p>In order to be considered for the <a href="https://2015.igem.org/Judging/Awards#SpecialPrizes">Best Model award</a>, you must fill out this page.</p>
 
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
 
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<p>
 
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Here are a few examples from previous teams:
 
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</p>
 
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<ul>
 
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
 
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
 
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</ul>
 
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<h1 style="text-align:center">Under construction</h1>
 
  
 
<!-- Our text below, the above is left for now if someone wants to have those as reference -->
 
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<p style="margin-top: 40px"> Modeling is an important part of synthetic biology. With good models, one can gain insight of the reaction before doing anything in the lab and maybe avoid doing useless work. Modeling helps us understand what is happening in our reaction and why. Our project consisted of a few quite different parts, and that gave us a natural way to divide our modeling to four parts. </p>
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<h1>Modeling</h1>
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<p style="margin-top: 40px"> Modeling is an important part of synthetic biology. With good models, one can gain insight of the biological phenomena before doing anything in the lab. Understanding the biological system allows us to make better decisions as we modify the system for our purposes. Our project consisted of quite a few different parts, and that gave us a natural way to divide our modeling to four parts. </p>
  
 
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<h2>Models of propane pathway</h2>
 
<h2>Models of propane pathway</h2>
  
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<p>The main thing of our project was the production of propane with e. coli, so the main task was to model the propane pathway. For more information, see our page of modelin propane.</p>
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<p>The main goal of our project was the production of propane with <span style="font-style:italic;">E. coli</span>, so the main task in modeling was to gain insight to the propane pathway. We made a model based on Michaelis-Menten enzyme kinetics and calculated numerical results from it with <a href="http://copasi.org/" target="_blank">Copasi</a> and <a href="http://se.mathworks.com/" target="_blank">Matlab</a>. With these tools we found the bottlenecks and most sensitive parts of our reaction. We also got some ideas on how much propane will be produced in our system. For more information, see our page of <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_propane">modeling propane pathway</a>.</p>
  
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<h2>Models of cellulose pathway</h2>
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<h2>Modeling synergy</h2>
  
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<p>We wanted to make propane from cellulose, so we also modeled the cellulose pathway in propane, see our page of modeling cellulose.</p>
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<figure style="float:right;margin-left:20px;">
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  <img src="https://static.igem.org/mediawiki/2015/e/e4/Aalto-Helsinki_micelle_circle_approach_2.png" style="width:200px;"/>
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</figure>
  
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<h2>Modeling synergy</h2>
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<p>One big concern in our project was the efficiency of propane production. To solve this problem we wanted to use micelles to hold enzymes together and speed up the reactions. We thought that this approach would be especially suitable for the of the last enzymes in our pathway, CAR and ADO, who have toxic butyraldehyde as a substrate between them. Since there are many butyraldehyde-consuming enzymes in a cell, the speed up we could get would be even better. We did a <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_synergy">stochastic model of synergy with python</a> to confirm if this approach would really work as we hope.</p>
  
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<p>One big concern in our project was the efficiency of propane production. To solve this problem we wanted to use micelles to hold enzymes together and speed up the reactions. We did a stochastic model of synergy with python.</p>
 
  
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<h2>Protein models</h2>
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<h2>Modeling micelle formation</h2>
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<p>As mentioned above we wanted to make propane production more efficient by having enzymes together in a micelle. While we modeled the efficiency of having the enzymes close together, we also wanted to know if it was possible to form the micelles with our enzymes in the first place. That is why we made a geometrical model about <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_micelle">micelle structure</a>.</p>
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<h2>Cellulose pathway</h2>
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<p style="padding-bottom:8%;margin-bottom:0;">Our project involves creating renewable propane from cellulose, which is why we wanted to model the pathway responsible for hydrolysing cellulose. The pathway consists of three different genes that cut the cellulose to glucose which the cell can then use as an energy source for propane production. While building a beneficial model of this wasn't possible for us, see our page of <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_cellulose">modeling cellulose pathway</a> to see what thoughts our modeling team had on this.</p>
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<!--<p>In our project we produce propane from cellulose, so our modeling team also <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_cellulose">took a look into cellulose pathway</a>.</p>-->
  
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<p>To gain insight on how the reactions work, we modeled the proteins in our propane pathway.</p>
 
  
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Latest revision as of 21:27, 18 September 2015

Modeling

Modeling is an important part of synthetic biology. With good models, one can gain insight of the biological phenomena before doing anything in the lab. Understanding the biological system allows us to make better decisions as we modify the system for our purposes. Our project consisted of quite a few different parts, and that gave us a natural way to divide our modeling to four parts.

Models of propane pathway

The main goal of our project was the production of propane with E. coli, so the main task in modeling was to gain insight to the propane pathway. We made a model based on Michaelis-Menten enzyme kinetics and calculated numerical results from it with Copasi and Matlab. With these tools we found the bottlenecks and most sensitive parts of our reaction. We also got some ideas on how much propane will be produced in our system. For more information, see our page of modeling propane pathway.

Modeling synergy

One big concern in our project was the efficiency of propane production. To solve this problem we wanted to use micelles to hold enzymes together and speed up the reactions. We thought that this approach would be especially suitable for the of the last enzymes in our pathway, CAR and ADO, who have toxic butyraldehyde as a substrate between them. Since there are many butyraldehyde-consuming enzymes in a cell, the speed up we could get would be even better. We did a stochastic model of synergy with python to confirm if this approach would really work as we hope.

Modeling micelle formation

As mentioned above we wanted to make propane production more efficient by having enzymes together in a micelle. While we modeled the efficiency of having the enzymes close together, we also wanted to know if it was possible to form the micelles with our enzymes in the first place. That is why we made a geometrical model about micelle structure.

Cellulose pathway

Our project involves creating renewable propane from cellulose, which is why we wanted to model the pathway responsible for hydrolysing cellulose. The pathway consists of three different genes that cut the cellulose to glucose which the cell can then use as an energy source for propane production. While building a beneficial model of this wasn't possible for us, see our page of modeling cellulose pathway to see what thoughts our modeling team had on this.