Benjamin Sanchez - genome-scale metabolic models
Benjamin Sanchez is a computational biologist, specialized in Systems Biology, working at Chalmers University of Technology. His main research topic focuses on genome-scale metabolic models (GEMs). The scientist gave a talk introducing GECKO, a method for adding so-called “enzyme constraints” into GEM.
In the past 20 years, genome-scale metabolic models (GEMs) have risen as computational tools for simulating all metabolic phenotypes that a cell can attain while respecting fundamental mass balances constraints. However, the number of metabolic states bound to only these constraints is infinite. Therefore, it becomes necessary to include additional condition-specific constraints. Moreover, we would like these constraints to reflect physical limitations inside the cell, avoiding arbitrary ad-hoc bounds. Enzyme constraints that are added into GEM limit reaction rates by the absolute abundance of enzymes (derived from proteomics data), and when used in a GEM of S. cerevisiae, prove to be crucial for explaining yeast physiology and computing enzyme usage in metabolism. Condition-specific enzyme-constrained models of yeast can give insights into enzyme usage under different environmental stresses, and as a tool for highlighting enzymes that play an important role in the metabolic response to stress.
GECKO platform is a fundamental tool for improved simulations in quantitative computational biology and is highly useful in basic systems biology for elucidating omics data, and in metabolic engineering for improving the predictive performance of GEMs. Finally, the developed model could also be used for testing the effects of varying specific enzyme levels on the production of a metabolite of interest, modifications that could be tested in vivo with recently developed techniques for fine-tuning transcription.