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Likelihood based observability analysis and confidence intervals for predictions of dynamic models
University of Freiburg, Germany.
University of Freiburg, Germany Helmholtz Zentrum Munchen, Germany .
Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
2012 (English)In: BMC Systems Biology, ISSN 1752-0509, Vol. 6, no 120Article in journal (Refereed) Published
Abstract [en]

Background: Predicting a systems behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. less thanbrgreater than less thanbrgreater thanResults: In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. less thanbrgreater than less thanbrgreater thanConclusions: The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at to)ckreutz/PPL.

Place, publisher, year, edition, pages
BioMed Central , 2012. Vol. 6, no 120
Keyword [en]
Confidence intervals, Identifiability, Likelihood, Parameter estimation, Prediction, Profile likelihood, Optimal experimental design, Ordinary differential equations, Signal transduction, Statistical inference, Uncertainty
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-86388DOI: 10.1186/1752-0509-6-120ISI: 000311225900001OAI: diva2:576868

Funding Agencies|BMBF|0315766-VirtualLiver0315415E-LungSys0313921-FRISYS|SBCancer DKFZ by the Helmholtz Society|V.2|German Research Foundation (DFG)||Albert Ludwigs University Freiburg||

Available from: 2012-12-14 Created: 2012-12-14 Last updated: 2013-01-14

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