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Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Stockholm University, Science for Life Laboratory (SciLifeLab). KTH Royal Institute of Technology, Sweden; Swedish e-Science Research Centre (SeRC), Sweden.
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Number of Authors: 72019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 2, p. 284-292Article in journal (Refereed) Published
Abstract [en]

Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours.

Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building.

Place, publisher, year, edition, pages
2019. Vol. 35, no 2, p. 284-292
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Biological Sciences Environmental Biotechnology Computer and Information Sciences Mathematics
Identifiers
URN: urn:nbn:se:su:diva-167686DOI: 10.1093/bioinformatics/bty607ISI: 000459314900013PubMedID: 30010712OAI: oai:DiVA.org:su-167686DiVA, id: diva2:1301730
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved

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