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Schmiester, L. ; Weindl, D. ; Hasenauer, J.

Efficient gradient-based parameter estimation for dynamic models using qualitative data.

Bioinformatics 37, 4493-4500 (2021)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
MOTIVATION: Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. RESULTS: Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. Additionally, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. AVAILABILITY: The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Network
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 37, Issue: 23, Pages: 4493-4500 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
European Union's Horizon 2020 research and innovation program (CanPathPro)