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Villaverde, A.F.* ; Raimundez-Alvarez, E. ; Hasenauer, J. ; Banga, J.R.*

A comparison of methods for quantifying prediction uncertainty in systems biology.

IFAC PapersOnline 52, 45-51 (2019)
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The parameters of dynamical models of biological processes always possess some degree of uncertainty. This parameter uncertainty translates into an uncertainty of model predictions. The trajectories of unmeasured state variables are examples of such predictions. Quantifying the uncertainty associated with a given prediction is an important problem for model developers and users. However, the nonlinearity and complexity of most dynamical models renders it nontrivial. Here, we evaluate three state-of-the-art approaches for prediction uncertainty quantification using two models of different sizes and computational complexities. We discuss the trade-offs between applicability and statistical interpretability of the different methods, and provide guidelines for their application.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Computational Methods ; Dynamic Models ; Nonlinear Systems ; Observability ; Prediction Error Methods ; State Estimation ; Uncertainty
ISSN (print) / ISBN 2405-8963
e-ISSN 1474-6670
Journal IFAC-PapersOnLine
Quellenangaben Volume: 52, Issue: 26, Pages: 45-51 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Frankfurt ; München [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)