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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)
Verlagsversion DOI
Open Access Gold
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Computational Methods ; Dynamic Models ; Nonlinear Systems ; Observability ; Prediction Error Methods ; State Estimation ; Uncertainty
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2405-8963
e-ISSN 1474-6670
Zeitschrift IFAC-PapersOnLine
Quellenangaben Band: 52, Heft: 26, Seiten: 45-51 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Frankfurt ; München [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553800-001
DOI 10.1016/j.ifacol.2019.12.234
Scopus ID 85081101272
Erfassungsdatum 2020-05-12
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