{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:00:23Z","timestamp":1768471223748,"version":"3.49.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T00:00:00Z","timestamp":1560902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"DYNAMICS","award":["ERA-IB-14-81"],"award-info":[{"award-number":["ERA-IB-14-81"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz500","type":"journal-article","created":{"date-parts":[[2019,6,12]],"date-time":"2019-06-12T19:12:56Z","timestamp":1560366776000},"page":"232-240","source":"Crossref","is-referenced-by-count":18,"title":["Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis"],"prefix":"10.1093","volume":"36","author":[{"given":"Axel","family":"Theorell","sequence":"first","affiliation":[{"name":"Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum J\u00fclich GmbH , J\u00fclich 52428, Germany"}]},{"given":"Katharina","family":"N\u00f6h","sequence":"additional","affiliation":[{"name":"Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum J\u00fclich GmbH , J\u00fclich 52428, Germany"}]}],"member":"286","published-online":{"date-parts":[[2019,6,19]]},"reference":[{"key":"2023013109500890500_btz500-B1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1287\/moor.18.2.255","article-title":"Hit-and-run algorithms for generating multivariate distributions","volume":"18","author":"B\u00e9lisle","year":"1993","journal-title":"Math. 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