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@ARTICLE{Theorell:866368,
      author       = {Theorell, Axel and Nöh, Katharina},
      title        = {{R}eversible jump {MCMC} for multi-model inference in
                      metabolic flux analysis},
      journal      = {Bioinformatics},
      volume       = {36},
      number       = {1},
      issn         = {0266-7061},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2019-05524},
      pages        = {232 - 240},
      year         = {2020},
      note         = {Post-Print nicht verfügbar!},
      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.},
      cin          = {IBG-1},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      pnm          = {583 - Innovative Synergisms (POF3-583)},
      pid          = {G:(DE-HGF)POF3-583},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31214716},
      UT           = {WOS:000508116000029},
      doi          = {10.1093/bioinformatics/btz500},
      url          = {https://juser.fz-juelich.de/record/866368},
}