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

• DOI: 10.48550/arxiv.2305.01821
• Publication Date: 2023-01-01
• Authors: Yannik Schälte; Fabian Fröhlich; Paul Jonas Jost; Jakob Vanhoefer; Dilan Pathirana; Paul Stapor; Polina Lakrisenko; Dantong Wang; Elba Raimúndez; Simon Merkt; Leonard Schmiester; Philipp Städter; Stephan Grein; Erika Dudkin; Domagoj Dorešić; Daniel Weindl; Jan Hasenauer
• Keywords: Python; Implementation; Interface (matter); MIT License; USable

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 complex systems. We present pyPESTO, a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, pyPESTO is broadly applicable black-box problems. Besides own implementations, it provides unified interface various popular simulation inference methods. implemented in Python, open-source under 3-Clause BSD license. Code documentation available GitHub (https://github.com/icb-dcm/pypesto).