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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)
Verlagsversion DOI PMC
Free by publisher
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 39, Heft: 11, Seiten: , Artikelnummer: btad711 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Joachim Herz Foundation
German Federal Ministry of Education and Research
Human Frontier Science Program
German Research Foundation