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Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways

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 sy...

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Autores principales: MacGillivray, Michael, Ko, Amy, Gruber, Emily, Sawyer, Miranda, Almaas, Eivind, Holder, Allen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427939/
https://www.ncbi.nlm.nih.gov/pubmed/28325918
http://dx.doi.org/10.1038/s41598-017-00170-3
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author MacGillivray, Michael
Ko, Amy
Gruber, Emily
Sawyer, Miranda
Almaas, Eivind
Holder, Allen
author_facet MacGillivray, Michael
Ko, Amy
Gruber, Emily
Sawyer, Miranda
Almaas, Eivind
Holder, Allen
author_sort MacGillivray, Michael
collection PubMed
description 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.
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spelling pubmed-54279392017-05-12 Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways MacGillivray, Michael Ko, Amy Gruber, Emily Sawyer, Miranda Almaas, Eivind Holder, Allen Sci Rep Article 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. Nature Publishing Group UK 2017-03-21 /pmc/articles/PMC5427939/ /pubmed/28325918 http://dx.doi.org/10.1038/s41598-017-00170-3 Text en © The Author(s) 2017 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
MacGillivray, Michael
Ko, Amy
Gruber, Emily
Sawyer, Miranda
Almaas, Eivind
Holder, Allen
Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title 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_short Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways
title_sort robust analysis of fluxes in genome-scale metabolic pathways
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427939/
https://www.ncbi.nlm.nih.gov/pubmed/28325918
http://dx.doi.org/10.1038/s41598-017-00170-3
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