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

Assessment of prediction uncertainty quantification methods in systems biology.

IEEE/ACM Trans. Comput. Biol. Bioinform. 20, 1725-1736 (2022)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitably possess some degree of uncertainty. In practical applications it is important to quantify these parameter uncertainties as well as the resulting prediction uncertainty, which are uncertainties of potentially time-dependent model characteristics. Unfortunately, estimating prediction uncertainties accurately is nontrivial, due to the nonlinear dependence of model characteristics on parameters. While a number of numerical approaches have been proposed for this task, their strengths and weaknesses have not been systematically assessed yet. To fill this knowledge gap, we apply four state of the art methods for uncertainty quantification to four case studies of different computational complexities. This reveals the trade-offs between their applicability and their statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Uncertainty; Biological system modeling; Mathematical models; Predictive models; Computational modeling; Data models; Uncertain systems; Computational methods; dynamic models; nonlinear systems; observability; prediction error methods; state estimation; uncertainty; Networks; Models
ISSN (print) / ISBN 1545-5963
e-ISSN 1557-9964
Journal IEEE/ACM Transactions on Computational Biology and Bioinformatics
Quellenangaben Volume: 20, Issue: 3, Pages: 1725-1736 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1314 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants
European Union's Horizon 2020 Research and Innovation Program (CanPathPro)
Germany's Excellence Strategy
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
ESF Investing in your future
MCIN/AEI
Conselleria~de Cultura, Educacion e Ordenacion Universitaria, Xunta de Galicia