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

 
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<img class="full_size_image" style="margin-top:-360px" src="https://static.igem.org/mediawiki/2018/7/70/T--SCUT-ChinaA--title3-2.png">
  
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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.
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<p style="text-align: justify"><a href="https://github.com/scutzhuzh/optool">Click Here!</a></p>
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<h2 style="text-align: left">Abstract</h2>
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To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to simulate our system, with the matrix of the pathway and the  \(V_{max}\) (calculated by \(k_{cat}\) and \(E_t\) )  of each reactions. And, inspired of machine learning algorithms, we established an algorithm using gradient descent method to search for the optimal solution of \(E_t\). Finally, we got results that were close to the results on some published articles we read, and hence we decided to design our experiment based on the model. Also, while building our model, we have developed a software tool which may be helpful for those who need to optimize a pathway.
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<h2 style="text-align: left">Input</h2>
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What you need:
 
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A complete metabolic pathway, and convert it into a mathematical form, a matrix \(S\) .
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The constants of the enzymes. Usually you need to put the \(k_{cat}\) and the \(E_t\) in.
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</ul>
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<p>You can find more details on how to use this software tool on README.md</p>
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<h2 style="text-align: left">Output</h2>
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What you will get is a figure like this:
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<img  src="https://static.igem.org/mediawiki/2018/5/56/T--SCUT-ChinaA--modelresult.jpg">
 
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<h2 style="text-align: left">Flux Balance Analysis</h2>
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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).
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To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the \(V_{max}\) (calculated by \(k_{cat}\) and \(E_t\) ) of each reactions. After we get the relationship we optimize the output by finding the best solution of \(E_t\) , using Newton method.
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You can also watch the software tool running as the picture below shows, and at the end the software tool will predict the rate of producing your product:
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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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<img  src="https://static.igem.org/mediawiki/2018/1/16/T--SCUT-ChinaA--pythonjietu.png">
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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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Latest revision as of 00:49, 18 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.

You can find more details on how to use this software tool on README.md

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).

You can also watch the software tool running as the picture below shows, and at the end the software tool will predict the rate of producing your product: