Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 454581
Title Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters
Author(s) Mourik, S. van; Braak, C.J.F. ter; Stigter, J.D.; Molenaar, J.
Source PeerJ 2 (2014). - ISSN 2167-8359 - 17 p.
DOI http://dx.doi.org/10.7717/peerj.433
Department(s) Farm Technology Group
Biometris (PPO/PRI)
Biometris (WU MAT)
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) identifiability analysis - regulatory networks - experimental-design - profile likelihood - sloppy models - oscillations - proteins - cyclin - kinase - cdc2
Abstract Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC) algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler) and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.
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