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

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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 <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_propane">modeling propane pathway</a>.</p>
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<p>The main thing 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://www.copasi.org/tiki-view_articles.php">Copasi</a> and <a href="http://se.mathworks.com/">Matlab</a>. With these tools we found the bottlenecks and most sensitive parts of our reaction. We also have some idea of 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>
  
 
<!-- Synergy -->
 
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</figure>
 
</figure>
  
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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 <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_synergy">stochastic model of synergy with python</a>.</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 thought that this approach would be especially suitable for the two last enzymes in our pathway, Car and Ado, who have toxic butyraldehyde as a substrate in between them. Since there are many butyraldehyde-eating 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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<h2>Modeling micelle formation</h2>
 
<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 this solution, we also wanted to know if the solution was possible in the first place. That is why we made a model about <a href="https://2015.igem.org/Team:Aalto-Helsinki/Modeling_micelle">micelle structure</a>.</p>
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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 this solution, we also wanted to know if the solution was possible 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>
  
 
<!-- Cellulose -->
 
<!-- Cellulose -->
 
<h2>Cellulose pathway</h2>
 
<h2>Cellulose pathway</h2>
  
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<p>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>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>
  
  

Revision as of 11:00, 5 August 2015

Modeling is an important part of synthetic biology. With good models, one can gain insight of the reaction before doing anything in the lab. By having a better understanding of the ideas that govern our project, we could see the influence of each compound in the reaction pathway and have a basis to make decisions that would have a long term impact in our results. Our project consisted of a few quite different parts, and that gave us a natural way to divide our modeling to four parts.

Models of propane pathway

The main thing 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 have some idea of 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 two last enzymes in our pathway, Car and Ado, who have toxic butyraldehyde as a substrate in between them. Since there are many butyraldehyde-eating 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 this solution, we also wanted to know if the solution was possible in the first place. That is why we made a geometrical model about micelle structure.

Cellulose pathway

In our project we produce propane from cellulose, so our modeling team also took a look into cellulose pathway.