Bayesian Metabolic Flux Analysis reveals intracellular flux couplings

TL;DR

This paper introduces a Bayesian approach to metabolic flux analysis that models the entire system probabilistically, revealing flux couplings and improving intracellular flux estimates over traditional methods.

Contribution

The novel Bayesian framework models genome-scale metabolic fluxes jointly, capturing uncertainties and flux couplings, and replaces traditional flux balance analysis methods.

Findings

Characterizes genome-scale flux covariances

Reveals intracellular flux couplings

Determines more unobserved fluxes from 13C data

Abstract

Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates. 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 target 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 flux balance analysis (FBA). Our experiments indicate that we can characterise the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in C. acetobutylicum from 13C data than flux variability analysis. The COBRA compatible software is available at github.com/markusheinonen/bamfa