Bayesian metabolic flux analysis reveals intracellular flux couplings

Tutkimustuotos: Lehtiartikkeli

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Bayesian metabolic flux analysis reveals intracellular flux couplings. / Heinonen, Markus; Osmala, Maria; Mannerström, Henrik; Wallenius, Janne; Kaski, Samuel; Rousu, Juho; Lähdesmäki, Harri.

julkaisussa: Bioinformatics, Vuosikerta 35, Nro 14, btz315, 15.07.2019, s. i548-i557.

Tutkimustuotos: Lehtiartikkeli

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Bibtex - Lataa

@article{ac8fc5b4f16046fcb47ce8c576ec6cd0,
title = "Bayesian metabolic flux analysis reveals intracellular flux couplings",
abstract = "Motivation: Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates. Results: We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.",
author = "Markus Heinonen and Maria Osmala and Henrik Mannerstr{\"o}m and Janne Wallenius and Samuel Kaski and Juho Rousu and Harri L{\"a}hdesm{\"a}ki",
year = "2019",
month = "7",
day = "15",
doi = "10.1093/bioinformatics/btz315",
language = "English",
volume = "35",
pages = "i548--i557",
journal = "Bioinformatics",
issn = "1367-4803",
number = "14",

}

RIS - Lataa

TY - JOUR

T1 - Bayesian metabolic flux analysis reveals intracellular flux couplings

AU - Heinonen, Markus

AU - Osmala, Maria

AU - Mannerström, Henrik

AU - Wallenius, Janne

AU - Kaski, Samuel

AU - Rousu, Juho

AU - Lähdesmäki, Harri

PY - 2019/7/15

Y1 - 2019/7/15

N2 - Motivation: Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates. Results: We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.

AB - Motivation: Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates. Results: We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.

UR - http://www.scopus.com/inward/record.url?scp=85068901722&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btz315

DO - 10.1093/bioinformatics/btz315

M3 - Article

VL - 35

SP - i548-i557

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 14

M1 - btz315

ER -

ID: 35580776