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| Hauptseite > Publikationsdatenbank > Reversible jump MCMC for multi-model inference in metabolic flux analysis |
| Hauptseite > Publikationsdatenbank > Reversible jump MCMC for multi-model inference in metabolic flux analysis |
| Journal Article | FZJ-2019-05524 |
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2020
Oxford Univ. Press
Oxford
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Please use a persistent id in citations: doi:10.1093/bioinformatics/btz500
Abstract: MotivationThe 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.ResultsHere 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.
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