Abstract Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the current most accurate models of biological systems include metabolism and expression (ME-models), and Expression and Thermodynamics FLux (ETFL) is one such formulation that efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli . To therefore adapt this ME-model for Saccharomyces cerevisiae , we herein developed yETFL. To do this, we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the predictive ability of yETFL to capture maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the extended ETFL formulation can be applied to ME-model development for a wide range of eukaryotic organisms. The utility of these ME-models can be extended into academic and industrial research.

A genome-scale metabolic model ofSaccharomyces cerevisiaethat integrates expression constraints and reaction thermodynamics