Auxotrophy-based curation improves the consensus genome-scale metabolic model of yeast

Abstract

Saccharomyces cerevisiae, a widely utilized model organism, has seen continuous updates to its genome-scale metabolic model (GEM) to enhance the prediction performance for metabolic engineering and systems biology. This study presents an auxotrophy-based curation of the yeast GEM, enabling facile upgrades to yeast GEMs in future endeavors. We illustrated that the curation bolstered the predictive capability of the yeast GEM particularly in predicting auxotrophs without compromising accuracy in other simulations, and thus could be an effective manner for GEM refinement. Last, we leveraged the curated yeast GEM to systematically predict auxotrophs, thereby furnishing a valuable reference for the design of nutrient-dependent cell factories and synthetic yeast consortia.