Human Genome-Scale Models of Metabolism and Gene Expression Reveal Resource Constraints of Cancer Cell Lines

Abstract

Genome-scale metabolic models (M-models) provide mechanistic insight into intracellular metabolism by simulating fluxes subject to nutrient and energy resource constraints. However, they cannot account for a major component of resource allocation, since they do not explicitly account for the cost of producing and maintaining enzymes. Genome-scale models of metabolism and gene expression (ME-Models) address this by including gene expression reactions, but these have only been developed for prokaryotes due to the additional complexity and challenges of modeling eukaryotes. Here, we present the human ME-Model, which encodes transcription, translation, complex formation, and turnover reactions for all enzymes catalyzing metabolic reactions, and couples these processes to constrain metabolic fluxes. We introduce humanME, a Python package to build and analyze human ME-Models. With this, we constructed 16 cancer cell line ME-Models. We found that resource constraints improve growth-rate predictions, and that ME-Model flux predictions are more biologically plausible and efficient. Moreover, transcriptional fluxes recapitulate RNA-Seq expression levels, with discrepancies revealing potential trade-offs involving multiple cellular objectives. Finally, the ME-Model recapitulates the Warburg effect, with increasing growth rate inducing glycolytic shifts, in part due to machinery costs of the electron transport chain. Altogether, we show ME-modeling can mechanistically link gene expression, resource allocation, and metabolism in human cells, substantially expanding the predictive scope of constraint-based models.