Computing Proteome Abundance and Activity with a Genome-Scale Model of Metabolism and Gene Expression

We have constructed a genome-scale model for Escherichia coli that seamlessly integrates metabolic and gene product expression pathways, termed an ME-Model. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth maximization to show that the ME-model accurately predicts biomass composition, carbon source uptake rates, by-product secretion rates, and central carbon metabolic fluxes. Importantly, the ME-model rectifies a number of failure modes of metabolic models by accounting for enzyme synthesis costs.

We have furthermore developed a method that utilizes the ME-Model to integrate gene expression and physiological data to predict condition-specific enzyme activities. We first utilize proteomic data from E. coli growth in batch culture with different carbon sources to derive a consistent genome-wide parameter set of effective enzyme activities. We show that the parameter set agrees with low-throughput kinetic assays and allows for prediction of absolute protein expression levels. We then utilize gene expression data from chemostat culture at various dilution rates to characterize the changes in enzyme activity under nutrient limitation.

The uses of the ME-Model described here will have important implications for metabolic engineering. We envision the ME-Model will aid in guiding genome reduction strategies, quantifying proteome burden, utilizing omics data to iteratively improve strains, and understanding the effects of culture conditions on organismal physiology.