Computing the functional proteome: recent progress and future prospects for genome-scale models

O’Brien, Edward J.; Palsson, Bernhard Ø.

doi:10.1016/j.copbio.2014.12.017

Cited by 71 publications

(55 citation statements)

References 98 publications

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“…One of the primary uses of ME models in previous studies has been to model optimal E. coli strains27. In particular, strains have been evolved in the laboratory while under environmental pressures designed to select for mutations that optimize for growth rate28.…”

Section: Discussionmentioning

confidence: 99%

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

Yang

Yurkovich

Lloyd

et al. 2016

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

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

Yang

Yurkovich

Lloyd

et al. 2016

Sci Rep

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“…However, more than 50% of the genes specific to the model-based core proteome were already nonmetabolic, highlighting the ability of the ME model-based approach to account for systemic gene interactions beyond metabolism. Therefore, as ME models continue to increase in biological scope (28,29), systems-level understanding of the core proteome is expected to broaden as well. We then characterized the defined core proteome in the context of transcriptomics or proteomics data across multiple growth conditions, strains, and genetic backgrounds.…”

Section: Discussionmentioning

confidence: 99%

“…Affinity propagation (36) was used to cluster simulated translation flux profiles (37). Enrichment analysis was performed by using hypergeometric P values, with the Benjamini-Hochberg correction for multiple testing (29). Statistical tests of difference between Pearson correlation coefficients were performed by using Fisher's Z-transform method (38) and Zou's confidence interval method (39).…”

Section: Methodsmentioning

confidence: 99%

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data

Yang¹,

Tan²,

O’Brien³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5′UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

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“…Like discoveries enabled by comparing M-Model predictions to experimental data, we anticipate much biology can be learned from comparing in silico and in vivo proteome allocation (O’Brien and Palsson, 2015), leading to increasingly predictive models. The E. coli ME-Model currently encompasses many key cellular functions, covering ~80% of the proteome by mass in conditions of exponential growth; the remaining proteome mass outside of the scope of the model can guide model expansion.…”

Section: Moving Beyond Metabolism To Molecular Biologymentioning

confidence: 99%

Using Genome-scale Models to Predict Biological Capabilities

O’Brien

Monk

Palsson

2015

Cell

636

529

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Constraint-based reconstruction and analysis (COBRA) methods at the genome-scale have been under development since the first whole genome sequences appeared in the mid-1990s. A few years ago this approach began to demonstrate the ability to predict a range of cellular functions including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome-scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This primer will get you started.

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Computing the functional proteome: recent progress and future prospects for genome-scale models

Cited by 71 publications

References 98 publications

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

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

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data

Using Genome-scale Models to Predict Biological Capabilities

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