Bayesian metabolic flux analysis reveals intracellular flux couplings

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.

DOI

10.1093/bioinformatics/btz315

Citation

Heinonen , M , Osmala , M , Mannerström , H , Wallenius , J , Kaski , S , Rousu , J & Lähdesmäki , H 2019 , ' Bayesian metabolic flux analysis reveals intracellular flux couplings ' , Bioinformatics , vol. 35 , no. 14 , btz315 , pp. i548-i557 . https://doi.org/10.1093/bioinformatics/btz315

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201907304543

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[article-cris] Perustieteiden korkeakoulu / SCI

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