Understanding Metabolism through GEMs and FBA

The development of the first-generation GSMMs [1]

Genome-scale metabolic models (GEMs) and Flux Balance Analysis (FBA) are complementary computational frameworks used to study and predict the metabolic behavior of living organisms. A GEM is a mathematical representation of an organism’s complete metabolic network, reconstructed from its genome and biochemical knowledge of known reactions. It allows for a systems-level knowledge of metabolism by capturing the connections between genes, enzymes, processes, and metabolites using a stoichiometric matrix. Once a GEM is built, FBA is applied to analyze and simulate metabolic fluxes within this network. FBA optimizes a biological goal, like cellular growth or metabolite production, by predicting the ideal distribution of fluxes that meet mass balance and physiological constraints using linear programming and constraint-based modeling. Together, GEM and FBA enable in silico experiments that can predict the impact of genetic modifications, environmental changes, or nutrient availability, making them powerful tools in systems biology, metabolic engineering, and drug discovery.   Applications for GEMs and FBA are numerous in the fields of biotechnology, medicine, and research. GEMs support insights into cellular processes and interspecies interactions by offering a comprehensive understanding of metabolism in pathogens, microbial communities, and individual organisms. They are instrumental in metabolic engineering for designing and optimizing microorganisms to produce biofuels, pharmaceuticals, and other valuable compounds. While FBA quantitatively forecasts how metabolic fluxes will alter in response to genetic or environmental changes, GEMs assist in identifying critical pathways and possible therapeutic targets in biological situations. FBA enhances metabolic efficiency and directs the design of logical strains by mimicking metabolic reprogramming, nutrient limitations, or metabolic reprogramming. When combined, these methods allow for the integration of multi-omics data, facilitate the prediction of phenotypes, and promote the creation of biological systems that are efficient and sustainable. [1] Ye, C., Wei, X., Shi, T., Sun, X., Xu, N., Gao, C. and Zou, W., 2022. Genome-scale metabolic network models: From first-generation to next-generation. Applied microbiology and biotechnology, 106(13), pp.4907-4920.