Synthetic biology enables engineering of microbes, plants, and animal cells to install new or redesigned natural biosynthetic routes to synthesize biologically-based products such as novel biofuels and pharmaceuticals. One challenge preventing synthetic biology from reaching its full potential is the need to keep in check the damage caused by unwanted chemical or enzymatic side-reactions. When unchecked, this damage can diminish yields of end-products and/or poison the cells making these products, referred to as metabolites. Consequently cells must either repair the damaged metabolites, or convert them into harmless compounds. Metabolite damage and its control is analogous to DNA and protein damage and repair, but is much more poorly understood. The goal of this project is to develop a better understanding of which metabolites are damaged, how cells repair damaged metabolites, and to develop computational models for predicting metabolite damage and repair. This project will contribute to the development of the next generation work-force by providing cross-disciplinary training of graduate students and post-doctoral fellows. The project will also develop a hands-on workshop on chemoinformatics for biologists of all career levels that will include participation of faculty from minority-serving institutions.
Chemical (i.e. non-enzymatic) or enzymatic side-reactions can convert metabolites to useless or toxic compounds, which requires cells to have systems to deal with these damage products. It is also clear that chemically-mediated metabolite damage can impose stress upon a cell to such an extent to influence fitness and possibly interfere with synthetic biology applications. Research suggests that there are far more metabolite damage reactions and damage-control systems than the few known so far. The goal of this project is to develop a better understanding of which metabolites are damaged and how cells repair damaged metabolites. To achieve this he goal this collaborative project will coordinate progress on: 1) building a public database of chemical reactions of metabolites with algorithms to predict such reactions analogous to what KEGG/BioCyc does for enzyme reactions; 2) development of a theory-driven approach to predict and validate damage-control genes and their mode of action; 3) developing metabolic models that predict how damage reactions potentially impact cellular physiology and synthetic biology efforts; and 4) identifying damage products among thousands of unknown peaks in metabolomics profiles, which will permit validation of predicted damaged metabolites based on computational algorithms.
