Speaker
Description
Genome-scale metabolic models (GEMs) are computable knowledge bases containing information of all biochemical reactions in an organism of interest. Typically, Flux Balance Analysis (FBA) is used to predict intracellular flux distributions corresponding to limiting substrate-efficient metabolic phenotypes. For fast-growing microbes (Escherichia coli, yeasts), alternative efficiency calculi (e.g., proteome efficiency) might govern their physiology in different growth regimes – inaccessible to classical FBA. Extensions of GEMs, called proteome-constrained GEMs (pcGEMs), on the contrary, capture complex metabolic phenotypes based on optimal biosynthetic resource allocation to the enzymatic machinery operating the metabolic network. We have constructed pcGEMs for yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe to identify constraints ruling the respiration-fermentation switch in aerobic growth, also known as Crabtree effect. Both yeasts started fermenting glucose into ethanol after hitting the mitochondrial protein capacity constraint. Contrary to Crabtree+ yeasts, this constraint was never hit in a pcGEM of a Crabtree- yeast Pichia kluyveri. Contrasting quantitative proteomics data with pcGEM predictions hinted to P. kluyveri possessing more catalytically efficient electron transport chain proteins, sustaining a respiratory phenotype even at fast growth.
