pyPESTO: a modular and scalable tool for parameter estimation for dynamic models

Abstract 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, w...

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Vydáno v:Bioinformatics (Oxford, England) Ročník 39; číslo 11
Hlavní autoři: Schälte, Yannik, Fröhlich, Fabian, Jost, Paul J, Vanhoefer, Jakob, Pathirana, Dilan, Stapor, Paul, Lakrisenko, Polina, Wang, Dantong, Raimúndez, Elba, Merkt, Simon, Schmiester, Leonard, Städter, Philipp, Grein, Stephan, Dudkin, Erika, Doresic, Domagoj, Weindl, Daniel, Hasenauer, Jan
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 01.11.2023
Témata:
Algorithms
Computer Simulation
Documentation
Models, Biological
Software
Uncertainty
ISSN:1367-4803, 1367-4811, 1367-4811
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Shrnutí:Abstract 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).
Bibliografie:ObjectType-Article-1
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btad711