%0 Journal Article
%A Theorell, Axel
%A Nöh, Katharina
%T Reversible jump MCMC for multi-model inference in metabolic flux analysis
%J Bioinformatics
%V 36
%N 1
%@ 0266-7061
%C Oxford
%I Oxford Univ. Press
%M FZJ-2019-05524
%P 232 - 240
%D 2020
%Z Post-Print nicht verfügbar!
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:31214716
%U <Go to ISI:>//WOS:000508116000029
%R 10.1093/bioinformatics/btz500
%U https://juser.fz-juelich.de/record/866368