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

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 scal...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689677/
https://www.ncbi.nlm.nih.gov/pubmed/37995297
http://dx.doi.org/10.1093/bioinformatics/btad711
Descripción
Sumario: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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