Abstract
Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.
Highlights
Schmidt et al recently published a proteomics data Resource[13] covering ~55% of the predicted E. coli genes (>9 5% by mass) under 22 experimental conditions
We show that sector-constrained ME models can compute the proteome composition of generalist E. coli in a variety of different growth environments
While the interactions between thousands of individual proteins is complex, the E. coli proteome has been shown to exhibit relatively simple relationships when proteins were grouped into meaningful “sectors”[18]
Summary
Schmidt et al recently published a proteomics data Resource[13] covering ~55% of the predicted E. coli genes (>9 5% by mass) under 22 experimental conditions. Using genome-scale models, we show how such proteomics resources can be used to reveal principles underlying proteome allocation. In anticipation of environmental change, generalist (wild-type) E. coli allocate a fraction of the proteome to non-growth related functions. Such allocation can be viewed as “hedging” against unknown enviromental challenges[14], reflecting the evolutionary history of the organism and its successful survival strategy. We develop a pragmatic approach for modeling the proteome allocation resulting from such complex cellular objectives following in the spirit of omics integration with genome-scale models. We show that sector-constrained ME models can compute the proteome composition of generalist E. coli in a variety of different growth environments. We compare the “optimal” versus the “generalist” proteomes to reveal principles underlying proteome allocation
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