Bayesian metabolic flux analysis reveals intracellular flux couplings
Research output: Contribution to journal › Article › Scientific › peer-review
- Centre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMys
- Professorship Lähdesmäki H.
- Department of Computer Science
- Finnish Center for Artificial Intelligence
- Probabilistic Machine Learning
- Helsinki Institute for Information Technology HIIT
- Professorship Kaski S.
- Centre of Excellence in Computational Inference, COIN
- Professorship Rousu J.
- Institute for Molecular Medicine Finland
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.
|Publication status||Published - 15 Jul 2019|
|MoE publication type||A1 Journal article-refereed|