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000866368 1001_ $$0P:(DE-Juel1)166254$$aTheorell, Axel$$b0$$ufzj
000866368 245__ $$aReversible jump MCMC for multi-model inference in metabolic flux analysis
000866368 260__ $$aOxford$$bOxford Univ. Press$$c2020
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000866368 520__ $$aMotivationThe validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging.ResultsHere we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference.
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000866368 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b1$$eCorresponding author$$ufzj
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