Bayesian Metabolic Flux Analysis reveals intracellular flux couplings

Markus Heinonen 1, 2, Maria Osmala 1, Henrik Mannerstr om 1, Janne Wallenius 3 Samuel Kaski 1, 2, Juho Rousu 1, 2 and Harri L ahdesm aki 1 1 Department of Computer Science, Aalto University, Espoo, 02150, Finland 2 Helsinki Institute for Information Technology, Finland 3 Institute for Molecular Medicine Finland, Helsinki, Finland Abstract Motivation: 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. 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. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plugin 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. Contact: markus.o.heinonen@aalto.fi 1 Introduction Metabolic modelling considers networks of up to thousands of chemical reactions that transform metabolite molecules within cellular organisms (Palsson, 2015). The key problem of metabolism is estimation of the reaction rates, or fluxes, of the system of the highly interdependent intracellular fluxes from measurements of few exchange fluxes that transfer nutrients or products between the external medium and the cell. The dominant approach to flux estimation is the celebrated Flux Balance Analysis (FBA) framework that finds reaction rates that maximise prespecified cellular growth function (Feist and Palsson, 2010), while assuming the cell is in a steady-state, where concentrations of intracellular metabolites do not change (Almaas et al., 2004). The FBA problem can be casted as a convenient and computationally efficient linear programming problem of solving a system of linear steady-state constraints while maximising a linear growth target (Orth et al., 2010), and where flux measurements can be encoded as constraints to the fluxes (Carreira et al., 2014).