Robust flux balance analysis of multiscale biochemical reaction networks

Sun, Yuekai; Fleming, Ronan M. T.; Thiele, Ines; Saunders, Michael A.

doi:10.1186/1471-2105-14-240

Cited by 23 publications

(21 citation statements)

References 14 publications

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“…10 to maximize growth rate to six decimal points. Because ME Models are ill-conditioned81034, we used the 128-bit (quad-precision) linear and nonlinear programming solver qMINOS 5.6263536. (The soplex solver37 is another viable option, as it uses iterative refinement to achieve the needed numerical precision.)…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…Butyrate and formate production varies drastically between the different F. prausnitzii strains (11), despite their having the same central fermentation pathways encoded in the genomes. In fact, inclusion of nonmetabolic cellular processes, such as regulation and macromolecular synthesis, with metabolic models has been shown to increase their predictive potential by reducing the set of feasible solutions significantly (49)(50)(51)(52)(53).…”

Section: Discussionmentioning

confidence: 99%

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

et al. 2014

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eThe human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

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“…The lifting technique of ref. 11 was applied to GlcAerWT to reduce some of the large matrix values. The aim of lifting is to remove the need for scaling (and hence magnified errors from unscaling), but with DQQ we do activate scaling in step D because steps Q1 and Q2 follow.…”

Section: Resultsmentioning

confidence: 99%

“…A lifting approach11 has been implemented to alleviate this difficulty with multiscale problems. Lifting reduces the largest matrix entries by introducing auxiliary constraints and variables.…”

mentioning

confidence: 99%

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Yang

Fleming

et al. 2017

Sci Rep

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Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

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Robust flux balance analysis of multiscale biochemical reaction networks

Cited by 23 publications

References 14 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

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

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