Difference between revisions of "Team:NCTU Formosa/Model"

   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 Growth Model	QS System Model	Mutagenicity Prediction

   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.