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However, the nonlinear nature of the <jats:sup>13<\/jats:sup>C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in \u201cnon-gaussian\u201d situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in <jats:sup>13<\/jats:sup>C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from <jats:sup>13<\/jats:sup>C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-<jats:sup>13<\/jats:sup>C MOMA and P-<jats:sup>13<\/jats:sup>C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011111","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T18:38:53Z","timestamp":1699641533000},"page":"e1011111","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6056-353X","authenticated-orcid":true,"given":"Tyler W. 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