Difference between revisions of "Team:SCUT-ChinaA/Software"

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{{SCUT-ChinaA/CSS}}
 
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{{SCUT-ChinaA/T3}}
 
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As you can see in the model page, we have successfully used our model to help us design our experiment. If you are interested in our model, you can use our python software tool and even modify it if you like. You can find it on the GitHub.
 
As you can see in the model page, we have successfully used our model to help us design our experiment. If you are interested in our model, you can use our python software tool and even modify it if you like. You can find it on the GitHub.
 
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<p><a href="https://github.com/scutzhuzh/optool">Click Here!</a></p>
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<p style="text-align: justify"><a href="https://github.com/scutzhuzh/optool">Click Here!</a></p>
 
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What you need:
 
What you need:
 
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<h2 style="text-align: left">Output</h2>
 
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What you will get is a figure like this:
 
What you will get is a figure like this:
 
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The ordinate indicates the multiple of the predicted product generation rate of the model, and the sequence below the abscissa indicates the priority of the enzymes (the left is the highest).
 
The ordinate indicates the multiple of the predicted product generation rate of the model, and the sequence below the abscissa indicates the priority of the enzymes (the left is the highest).
 
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<th> enzyme </th> <th> Substrate </th> <th> Turnover Number [1/s] </th> <th> KM Value [mM] </th>
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<td> ERG10</td> <td>acetyl-CoA</td> <td>2.1</td> <td>0.33</td> </tr>
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<td> ERG13</td> <td>acetoacetyl-CoA, acetyl-CoA</td> <td>4.6</td> <td>acetoacetyl-CoA:0.0014, acetyl-CoA:0.05</td> </tr>
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<td> HMG1</td> <td>hydroxymethylglutaryl-CoA</td> <td>0.023</td> <td>0.045</td> </tr>
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<td>ERG12</td> <td>mevalonate</td> <td>2.36</td> <td>0.012</td> </tr>
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<td> ERG8</td> <td>phosphomevalonate</td> <td>3.4</td> <td>0.0042</td> </tr>
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<td>ERG19</td> <td>(R,S)-5-diphosphomevalonate</td> <td>5.9</td> <td>0.0091</td> </tr>
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<td> NDPS1</td> <td>isopentenyl diphosphate</td> <td>0.14</td> <td>0.047</td> </tr>
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<img class="full_size_image" style="margin-top:200px" src="https://static.igem.org/mediawiki/2018/0/0e/T--SCUT-ChinaA--bottom.png">
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While building our model, we developed a software with python to help us find the optimal solution. After we finished, we modified the software and made it universal, which means it can be used by other researchers. The software is tiny and very easy to use, and even those who are not familiar with python can use it, for we have written a detailed document to tell people how to use it and put it on the Github with the software tool. The software has showed its power in our project, thus we think that more researchers may benefit from it. What's more, the code of the software is commented and easy to understand, which means you can modify it to do more things as well. So, we think this software should be considered and it worths a prize.
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Revision as of 18:01, 17 October 2018

SCUT-ChinaA

As you can see in the model page, we have successfully used our model to help us design our experiment. If you are interested in our model, you can use our python software tool and even modify it if you like. You can find it on the GitHub.

Click Here!

Input

What you need:

  • A complete metabolic pathway, and convert it into a mathematical form, a matrix \(S\) .
  • The constants of the enzymes. Usually you need to put the \(k_{cat}\) and the \(E_t\) in.

Output

What you will get is a figure like this:

The ordinate indicates the multiple of the predicted product generation rate of the model, and the sequence below the abscissa indicates the priority of the enzymes (the left is the highest).