Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways

Abstract Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a...

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Autores principales:	Michael MacGillivray, Amy Ko, Emily Gruber, Miranda Sawyer, Eivind Almaas, Allen Holder
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/564842ef1a2348a58192b8aacef1b541
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spelling	oai:doaj.org-article:564842ef1a2348a58192b8aacef1b5412021-12-02T12:31:49ZRobust Analysis of Fluxes in Genome-Scale Metabolic Pathways10.1038/s41598-017-00170-32045-2322https://doaj.org/article/564842ef1a2348a58192b8aacef1b5412017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00170-3https://doaj.org/toc/2045-2322Abstract Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.Michael MacGillivrayAmy KoEmily GruberMiranda SawyerEivind AlmaasAllen HolderNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-20 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael MacGillivray Amy Ko Emily Gruber Miranda Sawyer Eivind Almaas Allen Holder Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
description	Abstract Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.
format	article
author	Michael MacGillivray Amy Ko Emily Gruber Miranda Sawyer Eivind Almaas Allen Holder
author_facet	Michael MacGillivray Amy Ko Emily Gruber Miranda Sawyer Eivind Almaas Allen Holder
author_sort	Michael MacGillivray
title	Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_short	Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_full	Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_fullStr	Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_full_unstemmed	Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_sort	robust analysis of fluxes in genome-scale metabolic pathways
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/564842ef1a2348a58192b8aacef1b541
work_keys_str_mv	AT michaelmacgillivray robustanalysisoffluxesingenomescalemetabolicpathways AT amyko robustanalysisoffluxesingenomescalemetabolicpathways AT emilygruber robustanalysisoffluxesingenomescalemetabolicpathways AT mirandasawyer robustanalysisoffluxesingenomescalemetabolicpathways AT eivindalmaas robustanalysisoffluxesingenomescalemetabolicpathways AT allenholder robustanalysisoffluxesingenomescalemetabolicpathways
_version_	1718394279042220032

Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways

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