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Title: Principles of proteome allocation are revealed using proteomic data and genome-scale models

Journal Article · · Scientific Reports
DOI:https://doi.org/10.1038/srep36734· OSTI ID:1373286
 [1];  [1];  [1];  [1];  [2];  [3]
  1. Univ. of California, San Diego, La Jolla, CA (United States)
  2. Stanford Univ., Stanford, CA (United States)
  3. Univ. of California, San Diego, La Jolla, CA (United States); The Technical Univ. of Denmark, Horsholm (Denmark)
+ Show Author Affiliations

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. Furthermore, 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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0008701
OSTI ID:
1373286
Journal Information:
Scientific Reports, Vol. 6, Issue 1; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 21 works
Citation information provided by
Web of Science

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Cited By (8)

Genome-scale metabolic models applied to human health and disease: Genome-scale metabolic models journal June 2017
Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth journal July 2017
Formation of dominant mode by evolution in biological systems journal April 2018
Machine and deep learning meet genome-scale metabolic modeling journal July 2019
Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli journal December 2019
A quantitative method for proteome reallocation using minimal regulatory interventions journal July 2020
DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression journal January 2019
Formation of Dominant Mode by Evolution in Biological Systems journal October 2017

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