Team:Illinois-Tools/Modeling

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Alain Viel,<br>
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Orianna Bretschger,
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<br>Daad Saffarini,
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<br>Helen White,
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<br>Jason Lohmueller,
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<li class="first"><a href="https://2009.igem.org/Team:Illinois-Tools">Home</a></li>  
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<br>Kim de Mora,
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        <li><a href="https://2009.igem.org/Team:Illinois-Tools/Team" >The Team</a></li>  
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<br>Colleen Hansel,
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        <li><a href="https://2009.igem.org/Team:Illinois-Tools/Project">The Project</a></li>
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                                <li><a href="https://2009.igem.org/Team:Illinois-Tools/Modeling"class="active">Modeling</a></li>
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<br>Pam Silver,
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                                <li><a href="https://2009.igem.org/Team:Illinois-Tools/Notebook">Notebook</a></li> 
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<br>Tamara Brenner,
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<br>Harvard BioLabs
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<font size=1>Our project sought to combine the detecting capabilities of bacteria with the speed and ubiquity of electricity by creating an inducible system in Shewanella oneidensis MR-1 with an electrical output, allowing for the direct integration of this biosensor with electrical circuits via microbial fuel cells.</font>
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Shewanella oneidensis MR-1 <br>
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(fondly referred to as Shewie)<br>
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is a metabolically versatile, <br>
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and genetically tractable, gram-<br>
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negative facultative anaerobe which under <br>
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acceptors.  This ability can be harnessed by <br>
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microbial fuel cells to produce an electric current.
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The broad goal of our project was to engineer S. Oneidensis to produce a detectable change in electric current in response to some environmental stimulus. In order to observe such a reaction, our first task was to design an environment capable of housing bacteria and measuring current production. The answer? Microbial fuel cells.
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=='''Modeling'''==
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The Illinois - Tools team can model any pathway, whose starting and ending compounds are stored in the Kegg database. The algorithm developed by the team takes information from the Kegg database and finds the most optimum pathway, based on the weights selected by the user. Examples include the pathway with the least number of steps, or the pathway that uses the least amount of ATP.
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The Illinois-Tools team also wishes to use this algorithm to help other IGEM teams in modeling their own desired pathways.  For example, the our program can help the Illinois wetlab team in modeling their pathway.  The wetlab team's project is about a binary decoder in the organism E. coli, that senses 2 inputs, such as 2 sugars, and produces one of four possible outputs, which are fluorescent proteins, based on the combination of inputs. 
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Go to [https://2009.igem.org/Team:Illinois Illinois]
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A possible future expansion of their project would be to model these pathways computationally, and see if it would be experimentally feasible. Furthermore, once the protein output is obtained, they want to check if it can be used to produce useful compounds like biofuels.
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To help this team reach its goals, our program, IMPtools, was able to model a pathway from D-Arabinose to Ethanol in E. coli.  The result can be seen below.  It has been optimized for E.coli, and can be further optimized when the wetlab team is ready to work on it.  The labels on the reaction are the Kegg IDs for compounds. 
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[[Image:Illinoistoolsarabinosetoethanolpathway.jpg|center|]]
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===The Algorithm===
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In modeling for other teams, a few things should be considered. The algorithm requires interactivity. It is can only optimize if you set the parameters to specify what you mean by "optimal". In that light, the user of IMPtools when modeling for a practical design application should consider how important removing excess reactants or creating biproducts could be, as well as the implications of ATP consumption. Sometimes the algorithm can return intermediate compounds that are truly only cofactors of a main reaction (these can generally be avoided by weighting against high order nodes, but also by specifically removing certain nodes). With IMPtools and a small amount of manipulation, a reasonable pathway can be returned using IMP's pathfinding algorithm. Using these results, actual experimentation is necessary to validate the pathway - perhaps, given future feedback the algorithm could account for user's successes and failures and preferentially bias results toward those experimentally validated.
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Latest revision as of 03:46, 22 October 2009

Modelingillinoistools.gif

Modeling

The Illinois - Tools team can model any pathway, whose starting and ending compounds are stored in the Kegg database. The algorithm developed by the team takes information from the Kegg database and finds the most optimum pathway, based on the weights selected by the user. Examples include the pathway with the least number of steps, or the pathway that uses the least amount of ATP.

The Illinois-Tools team also wishes to use this algorithm to help other IGEM teams in modeling their own desired pathways. For example, the our program can help the Illinois wetlab team in modeling their pathway. The wetlab team's project is about a binary decoder in the organism E. coli, that senses 2 inputs, such as 2 sugars, and produces one of four possible outputs, which are fluorescent proteins, based on the combination of inputs.

Go to Illinois

A possible future expansion of their project would be to model these pathways computationally, and see if it would be experimentally feasible. Furthermore, once the protein output is obtained, they want to check if it can be used to produce useful compounds like biofuels.

To help this team reach its goals, our program, IMPtools, was able to model a pathway from D-Arabinose to Ethanol in E. coli. The result can be seen below. It has been optimized for E.coli, and can be further optimized when the wetlab team is ready to work on it. The labels on the reaction are the Kegg IDs for compounds.

Illinoistoolsarabinosetoethanolpathway.jpg


The Algorithm

In modeling for other teams, a few things should be considered. The algorithm requires interactivity. It is can only optimize if you set the parameters to specify what you mean by "optimal". In that light, the user of IMPtools when modeling for a practical design application should consider how important removing excess reactants or creating biproducts could be, as well as the implications of ATP consumption. Sometimes the algorithm can return intermediate compounds that are truly only cofactors of a main reaction (these can generally be avoided by weighting against high order nodes, but also by specifically removing certain nodes). With IMPtools and a small amount of manipulation, a reasonable pathway can be returned using IMP's pathfinding algorithm. Using these results, actual experimentation is necessary to validate the pathway - perhaps, given future feedback the algorithm could account for user's successes and failures and preferentially bias results toward those experimentally validated.