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Hierarchical optimization for the efficient parametrization of ODE models.

Bioinformatics 34, 4266-4273 (2018)
Publ. Version/Full Text Postprint Research data DOI
Open Access Green
Motivation: Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems.Results: In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Parameter-estimation; Division; Systems
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 34, Issue: 24, Pages: 4266-4273 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Reviewing status