Team:NCTU Formosa

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   Many potential mutagenic substances exist in the environment, and they may cause damage to DNA in chemical or physical ways. Accumulating mutagenic harm may lead to gene mutations, causing cell damage, death, or transformation. Therefore, this year, our team constructed a system that quantitatively analyzed mutagenicity by combining the gene circuit design with the AI model. For all chemicals that already have known structures, we used the AI system to analyze its mutagenicity quantitatively. We built up our powerful AI with the results we found after paper research indicating that substructures contribute to mutation.

   Moreover, we used a chemical mutagenicity database and support vector machine algorithm to train the AI. It enabled us to give out precise mutagenicity with users inputting chemical names. Concerning a mutagenic factor with an unknown structure or an unidentified target, for example, UV light, radiation, and complex chemical compounds, by using our designed gene circuit and model, we could also perform qualitative and quantitative analysis. What we have done, essentially, was to transform toxin genes into E. coli, respectively, and observe its growth with O.D. measurement. According to the designed gene circuit, when the transformed E. coli faced a situation with mutagen, the growth would show its specific growing process. After documenting and gathering the results from different situations and model simulations, we could thus put the data into qualitative and quantitative analysis.

   For chemicals that already have known structures, a Mutagenicity Prediction AI based on relationships between chemical structures and mutagenicity was developed after paper surveying. Moreover, we collected specify chemical structures as training data and the result of Ames test as target data for machine learning by Support Vector Machine algorithm (SVM). Enabling the public to learn mutagenicity with a few clicks online with our user-friendly user interface.