ODE building is a powerful mean of characterizing biochemical reaction processes in biological systems. In this iGEM project, we built three equations based on the system under study to characterize the reaction of tetrazine and TCO, the nucleation process of CsgA, and the surface presentation of unnatural amino acids. Based on these equations, we were able to establish a more systematic understanding of the biochemical reaction system and to understand the factors in the system that affect the rate at which the biochemical reaction proceeds.

Quantum chemical computation is a powerful tool for systems with a small number of atoms that involve chemical reactions. In our project, with the help of Gaussian, a powerful software for quantum computation, we have successfully applied the means of quantum mechanics to the study of bioorthogonal cleavage reaction (BCR) .Also guided by the principles of chemical transition state theory, we used Gaussian to perform transition state search and activation energy calculations for the tetrazine-TCO triggered BCR involved in our project at a very specific level. The final value falls within the moderate to high range for IEDDA reactions, consistent with the experimental data. Anyway, the BCR plays a more and more important role in the field of chemical biology, and our practice has greatly improved our understanding of the IEDDA triggered BCR in our project.

Molecular dynamics is another simulation technique that is better suited to the simulation of dynamic physical processes in medium- to large-scale atomic systems than the low atomic number chemical reaction processes that quantum mechanical simulations specialize in. In our project, we successfully predicted the most suitable insertion site of Tet unnatural amino acid by molecular dynamics simulation using the molecular dynamics simulation tool Gromacs, which was verified in wet experiments. In addition, we simulated the dimerization nucleation process of CsgA, depicting the free energy landscape of the process.

With the rapid development of AI, machine learning and deep learning techniques are playing an increasingly important role in the field of biology research, and the modeling of deep learning patterns is also an effective means of data analysis in synthetic biology. In our project, members of the wet lab department, had sought advice from the dry lab department about screening promoters of expected strength, so we came up with the idea of applying machine learning techniques to predict promoter strength. As a result, Deepro, a promoter strength prediction framework that integrates multiple machine learning methods, was born.