{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-metabolic-flux-analysis-reveals","title":"Bayesian Metabolic Flux Analysis reveals intracellular flux couplings","arxiv_id":"1804.06673","date":"2018-04-18","proceeding":null,"authors":["Markus Heinonen","Maria Osmala","Henrik Mannerström","Janne Wallenius","Samuel Kaski","Juho Rousu","Harri Lähdesmäki"],"abstract":"Metabolic flux balance analyses are a standard tool in analysing metabolic\nreaction rates compatible with measurements, steady-state and the metabolic\nreaction network stoichiometry. Flux analysis methods commonly place\nunrealistic assumptions on fluxes due to the convenience of formulating the\nproblem as a linear programming model, and most methods ignore the notable\nuncertainty in flux estimates. We introduce a novel paradigm of Bayesian\nmetabolic flux analysis that models the reactions of the whole genome-scale\ncellular system in probabilistic terms, and can infer the full flux vector\ndistribution of genome-scale metabolic systems based on exchange and\nintracellular (e.g. 13C) flux measurements, steady-state assumptions, and\ntarget function assumptions. The Bayesian model couples all fluxes jointly\ntogether in a simple truncated multivariate posterior distribution, which\nreveals informative flux couplings. Our model is a plug-in replacement to\nconventional metabolic balance methods, such as flux balance analysis (FBA).\nOur experiments indicate that we can characterise the genome-scale flux\ncovariances, reveal flux couplings, and determine more intracellular unobserved\nfluxes in C. acetobutylicum from 13C data than flux variability analysis. The\nCOBRA compatible software is available at github.com/markusheinonen/bamfa","url_abs":"http://arxiv.org/abs/1804.06673v1","url_pdf":"http://arxiv.org/pdf/1804.06673v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-metabolic-flux-analysis-reveals","repo_url":"https://github.com/markusheinonen/bamfa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}