Since the birth of synthetic biology in 2000, metabolic engineering has made increasingly huge progress on high value-added natural products. The synthesis and construction of metabolic pathways play a fundamental role in microbiology, biochemistry, and many other relevant fields. In 2003, Jay Keasling’s team reconstructed the biological synthesis pathway of arteannuinic acid in E. coli, providing functional confirmation for this metabolic process. In recent years, researchers have engineered yeast to produce a variety of plant-based natural products. These pathways are likely to involve the introduction of many heterologous genes and numerous genetic modifications to increase productivity. In the biosynthesis of opioids in yeast, 17 enzymes from different plants, animals, and yeasts were encoded through synthetic biological way. Although there are several tools for scientists to analyze and select the potential genes of the designed pathway, these tools all focus on the one or two specific gene instead of the whole metabolic system. Therefore, what our team’s trying to do is making comparisons between the designed pathway we want to synthesize and the natural-existing pathways to make sure that the researchers can get the whole picture. In this way, scientists can learn more about molecular functions and biological process from an innovating aspect.
During the project, we had the pleasure to interview a PhD student in the university of Toronto. Based on his experiences working on biological metabolism, changing from several different software to visualize the network, do simulations or other studies, is quite inconvenient. Therefore, he suggested us to make our tool more handful for researches by integrating a few frequently-used functions together. Thanks to his advice, we added a new function into our project – SBML Drawer. SBML Drawer can visualize networks by taking xml files as input.
Then, we took a survey among students in the biology department in our school, and selected another two commonly-needed functions to combine into the toolkit, including SMILES Drawer and Gene Editor. People taking our survey were reported of having the need to compare the similarities between metabolites. SMILES Drawer enables people to calculate the similarity coefficient between metabolites, and preview their molecular structures. At the same time, Gene Editor was created to help researchers simulate the process of enzyme digestion, DNA translation and manage Genbank features of genomes they study.
Finally, from some feedbacks on our project, we made SBML Differ. After comparison of two networks, SBML Differ aims to visualize the difference between biological models, providing users with a clearer picture about network similarity apart from their similarity coefficient.