Difference between revisions of "Team:NCTU Formosa/Model"
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| − | + | <meta charset="UTF-8"> | |
| − | + | <title>Model Overview</title> | |
| − | + | <link rel="stylesheet" type="text/css" href="modelcss.css"> | |
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<body> | <body> | ||
| − | + | <div class="wrapper"> | |
| − | + | <video width="100%" muted autoplay> | |
| − | + | <source src="https://2019.igem.org/wiki/images/e/e1/T--NCTU_Formosa--Model.mp4" type="video/mp4"> | |
| − | + | <!-- <source src="notebookv.ogg" type="video/ogg"> --> | |
| − | + | Your browser does not support the video tag. | |
| − | + | </video> | |
| − | + | <div class="sec1"> | |
| − | + | <div class="title"> | |
| − | + | <p>Overview</p> | |
| − | + | </div> | |
| − | + | <p>   Modeling is calculating the physical phenomenon by using mathematical methods or logical | |
| − | + | algorithms. Our model aims at computing bacterial growth curve and predicting quorum sensing. Besides, | |
| − | + | we also develop an AI which can accurately predict the mutagenicity. Our model can be divided into the | |
| − | + | following three parts:</p> | |
| − | + | <table id="modtab"> | |
| − | + | <tr> | |
| + | <td><img src="https://2019.igem.org/wiki/images/a/ae/T--NCTU_Formosa--easymodel1.png"></td> | ||
| + | <td><img src="https://2019.igem.org/wiki/images/e/e7/T--NCTU_Formosa--easymodel2.png"></td> | ||
| + | <td><img src="https://2019.igem.org/wiki/images/6/65/T--NCTU_Formosa--easymodel3.png"></td> | ||
| + | |||
| + | </tr> | ||
| + | <tr> | ||
| + | <td><a href="https://2019.igem.org/Team:NCTU_Formosa/Growth_Model"> | ||
| + | <p>Computational<br>Growth Model</p> | ||
| + | </a></td> | ||
| + | <td><a href="https://2019.igem.org/Team:NCTU_Formosa/QS_Model"> | ||
| + | <p>QS System Model</p> | ||
| + | </a></td> | ||
| + | <td><a href="https://2019.igem.org/Team:NCTU_Formosa/Mutagenicity_Prediction"> | ||
| + | <p>Mutagenicity<br>Prediction</p> | ||
| + | </a></td> | ||
| + | |||
| + | </tr> | ||
| + | </table> | ||
| + | |||
| + | <p>   First, computational growth model simulates the result of our experiment. Mainly focusing on | ||
| + | the function of substance toxicity, nutrient limit, and mutation rate.</p> | ||
| + | <p>   On top of that, quorum sensing model predicts the effect of mutagenicity to the time of red | ||
| + | fluorescence.</p> | ||
| + | <p>   Furthermore, we develop an AI to predict the mutagenicity by analyzing the chemical | ||
| + | structure. Take our database of SMILES and Ames test result as training data for machine learning, and | ||
| + | then we use support vector machine (SVM) algorithm to train our model to accurately predict the | ||
| + | mutagenicity.</p> | ||
| + | <p>   All in all, for unknown chemical structure, we can measure it by our engineered <i>E. | ||
| + | coli</i>, and calculate the time of red fluorescence to get our quantified mutagenicity. For known | ||
| + | chemical structure, we can quickly get the mutagenicity by using the result of machine learning.</p> | ||
</div> | </div> | ||
| − | <p> | + | <img id="flow" src="https://2019.igem.org/wiki/images/6/6c/T--NCTU_Formosa--model_flow.png"> |
| − | + | <p class="explanation"> | |
| − | <p> | + | <svg class="icon" aria-hidden="true" data-prefix="fas" data-icon="arrow-circle-up" |
| − | < | + | class="svg-inline--fa fa-arrow-circle-up fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" |
| + | viewBox="0 0 512 512"> | ||
| + | <path fill="currentColor" | ||
| + | d="M8 256C8 119 119 8 256 8s248 111 248 248-111 248-248 248S8 393 8 256zm143.6 28.9l72.4-75.5V392c0 13.3 10.7 24 24 24h16c13.3 0 24-10.7 24-24V209.4l72.4 75.5c9.3 9.7 24.8 9.9 34.3.4l10.9-11c9.4-9.4 9.4-24.6 0-33.9L273 107.7c-9.4-9.4-24.6-9.4-33.9 0L106.3 240.4c-9.4 9.4-9.4 24.6 0 33.9l10.9 11c9.6 9.5 25.1 9.3 34.4-.4z"> | ||
| + | </path> | ||
| + | </svg> | ||
| + | Figure 1: The workflow of our model</p> | ||
| + | <div class="title_5"> | ||
| + | <p>Go to >>></p> | ||
| + | </div> | ||
| + | <div class="next"><a href="http://2019.igem.org/Team:NCTU_Formosa/Growth_Model"> | ||
| + | <p>Growth Model</p> | ||
| + | </a></div> | ||
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{{NCTU_Formosa/Footer}} | {{NCTU_Formosa/Footer}} | ||
{{NCTU_Formosa/title}} | {{NCTU_Formosa/title}} | ||
Revision as of 17:27, 21 October 2019
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Overview
Modeling is calculating the physical phenomenon by using mathematical methods or logical algorithms. Our model aims at computing bacterial growth curve and predicting quorum sensing. Besides, we also develop an AI which can accurately predict the mutagenicity. Our model can be divided into the following three parts:
 |
 |
 |
|
Computational |
QS System Model |
Mutagenicity |
First, computational growth model simulates the result of our experiment. Mainly focusing on the function of substance toxicity, nutrient limit, and mutation rate.
On top of that, quorum sensing model predicts the effect of mutagenicity to the time of red fluorescence.
Furthermore, we develop an AI to predict the mutagenicity by analyzing the chemical structure. Take our database of SMILES and Ames test result as training data for machine learning, and then we use support vector machine (SVM) algorithm to train our model to accurately predict the mutagenicity.
All in all, for unknown chemical structure, we can measure it by our engineered E. coli, and calculate the time of red fluorescence to get our quantified mutagenicity. For known chemical structure, we can quickly get the mutagenicity by using the result of machine learning.
Figure 1: The workflow of our model
Go to >>>
