pyPESTO is a widely applicable and highly customizable toolbox for parameter estimation.

pyPESTO features include:

• Multi-start local optimization
• Profile computation
• Result visualization
• Interface to AMICI for efficient simulation and sensitivity analysis of ordinary differential equation (ODE) models (example)
• Parameter estimation pipeline for systems biology problems specified in SBML and PEtab (example)
• Parameter estimation with ordinal data as described in Schmiester et al. (2020) and Schmiester et al. (2021). (example)
• Parameter estimation with censored data. (example)
• Parameter estimation with nonlinear-monotone data. (example)

The simplest way to install pyPESTO is via pip:

pip3 install pypesto

More information is available here: https://pypesto.readthedocs.io/en/latest/install.html

The documentation is hosted on readthedocs.io: https://pypesto.readthedocs.io

Multiple use cases are discussed in the documentation. In particular, there are jupyter notebooks in the doc/example directory.

We are happy about any contributions. For more information on how to contribute to pyPESTO check out https://pypesto.readthedocs.io/en/latest/contribute.html

Citeable DOI for the latest pyPESTO release:

When using pyPESTO in your project, please cite

• Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P., Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P., Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. (2023). pyPESTO: A modular and scalable tool for parameter estimation for dynamic models, Bioinformatics, 2023, btad711, doi:10.1093/bioinformatics/btad711

When presenting work that employs pyPESTO, feel free to use one of the icons in doc/logo/:

There is a list of publications using pyPESTO. If you used pyPESTO in your work, we are happy to include your project, please let us know via a GitHub issue.

pyPESTO supersedes PESTO a parameter estimation toolbox for MATLAB, whose development is discontinued.