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Schälte, Y. ; Fröhlich, F.* ; Jost, P.J.* ; Vanhoefer, J.* ; Pathirana, D.* ; Stapor, P. ; Lakrisenko, P. ; Wang, D. ; Raimundez-Alvarez, E. ; Merkt, S.* ; Schmiester, L. ; Städter, P. ; Grein, S.* ; Dudkin, E.* ; Doresic, D.* ; Weindl, D. ; Hasenauer, J.

pyPESTO: A modular and scalable tool for parameter estimation for dynamic models.

Bioinformatics 39:btad711 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
SUMMARY: Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. AVAILABILITY AND IMPLEMENTATION: pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 1367-4803
e-ISSN 1367-4811
Journal Bioinformatics
Quellenangaben Volume: 39, Issue: 11, Pages: , Article Number: btad711 Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Joachim Herz Foundation
German Federal Ministry of Education and Research
Human Frontier Science Program
German Research Foundation