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- Title
Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis.
- Authors
Theorell, Axel; Nöh, Katharina
- Abstract
Motivation 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. Results 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. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
BRITISH Medical Association; METABOLIC flux analysis; MONTE Carlo method; MARKOV chain Monte Carlo; PHENOMENOLOGICAL biology
- Publication
Bioinformatics, 2020, Vol 36, Issue 1, p232
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz500