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

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 metabol...

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Autores principales: Yang, Laurence, Yurkovich, James T., Lloyd, Colton J., Ebrahim, Ali, Saunders, Michael A., Palsson, Bernhard O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114563/
https://www.ncbi.nlm.nih.gov/pubmed/27857205
http://dx.doi.org/10.1038/srep36734
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author Yang, Laurence
Yurkovich, James T.
Lloyd, Colton J.
Ebrahim, Ali
Saunders, Michael A.
Palsson, Bernhard O.
author_facet Yang, Laurence
Yurkovich, James T.
Lloyd, Colton J.
Ebrahim, Ali
Saunders, Michael A.
Palsson, Bernhard O.
author_sort Yang, Laurence
collection PubMed
description 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 σ(S). 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.
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spelling pubmed-51145632016-11-25 Principles of proteome allocation are revealed using proteomic data and genome-scale models Yang, Laurence Yurkovich, James T. Lloyd, Colton J. Ebrahim, Ali Saunders, Michael A. Palsson, Bernhard O. Sci Rep Article 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 σ(S). 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. Nature Publishing Group 2016-11-18 /pmc/articles/PMC5114563/ /pubmed/27857205 http://dx.doi.org/10.1038/srep36734 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Yang, Laurence
Yurkovich, James T.
Lloyd, Colton J.
Ebrahim, Ali
Saunders, Michael A.
Palsson, Bernhard O.
Principles of proteome allocation are revealed using proteomic data and genome-scale models
title Principles of proteome allocation are revealed using proteomic data and genome-scale models
title_full Principles of proteome allocation are revealed using proteomic data and genome-scale models
title_fullStr Principles of proteome allocation are revealed using proteomic data and genome-scale models
title_full_unstemmed Principles of proteome allocation are revealed using proteomic data and genome-scale models
title_short Principles of proteome allocation are revealed using proteomic data and genome-scale models
title_sort principles of proteome allocation are revealed using proteomic data and genome-scale models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114563/
https://www.ncbi.nlm.nih.gov/pubmed/27857205
http://dx.doi.org/10.1038/srep36734
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