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. We present pyPESTO, 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. 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).
@article{arxiv.2305.01821,
title = {pyPESTO: A modular and scalable tool for parameter estimation for dynamic models},
author = {Yannik Schälte and Fabian Fröhlich and Paul J. Jost and Jakob Vanhoefer and Dilan Pathirana and Paul Stapor and Polina Lakrisenko and Dantong Wang and Elba Raimúndez and Simon Merkt and Leonard Schmiester and Philipp Städter and Stephan Grein and Erika Dudkin and Domagoj Doresic and Daniel Weindl and Jan Hasenauer},
journal= {arXiv preprint arXiv:2305.01821},
year = {2023}
}