Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
This provides a more accurate and uncertainty-aware method for researchers in metabolic engineering and systems biology, though it is an incremental improvement as a plug-in replacement to existing flux balance analysis methods.
The authors tackled the problem of unrealistic assumptions and ignored uncertainty in metabolic flux analysis by introducing a Bayesian approach that models genome-scale metabolic systems probabilistically, resulting in the ability to infer full flux vector distributions and reveal flux couplings, with experiments showing they can determine more intracellular unobserved fluxes in C. acetobutylicum from 13C data than flux variability analysis.
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