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| 001 | 866368 | ||
| 005 | 20210112185708.0 | ||
| 024 | 7 | _ | |a 10.1093/bioinformatics/btz500 |2 doi |
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| 100 | 1 | _ | |a Theorell, Axel |0 P:(DE-Juel1)166254 |b 0 |u fzj |
| 245 | _ | _ | |a Reversible jump MCMC for multi-model inference in metabolic flux analysis |
| 260 | _ | _ | |a Oxford |c 2020 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Nöh, Katharina |0 P:(DE-Juel1)129051 |b 1 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1093/bioinformatics/btz500 |g p. btz500 |0 PERI:(DE-600)1468345-3 |n 1 |p 232 - 240 |t Bioinformatics |v 36 |y 2020 |x 0266-7061 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/866368/files/btz500.pdf |y Restricted |
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