pyPESTO - Parameter EStimation TOolbox for python

pyPESTO logo

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

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Feature overview

Feature overview of pyPESTO. Figure taken from the Bioinformatics publication.

pyPESTO features include:

  • Parameter estimation interfacing multiple optimization algorithms including multi-start local and global optimization. (example, overview of optimizers)
  • Interface to multiple simulators including
    • AMICI for efficient simulation and sensitivity analysis of ordinary differential equation (ODE) models. (example)
    • RoadRunner for simulation of SBML models. (example)
    • Jax and Julia for automatic differentiation.
  • Uncertainty quantification using various methods:
    • Profile likelihoods.
    • Sampling using Markov chain Monte Carlo (MCMC), parallel tempering, and interfacing other samplers including emcee, pymc and dynesty. (example)
    • Variational inference
  • Complete parameter estimation pipeline for systems biology problems specified in SBML and PEtab. (example)
  • Parameter estimation pipelines for different modes of data:
  • Model selection. (example)
  • Various visualization methods to analyze parameter estimation results.

Quick install

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

Documentation

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

Examples

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

Contributing

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

How to Cite

Citeable DOI for the latest pyPESTO release: DOI

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. pyPESTO: A modular and scalable tool for parameter estimation for dynamic models, Bioinformatics, Volume 39, Issue 11, 2023, btad711, doi:10.1093/bioinformatics/btad711

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

pyPESTO 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.

References

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

Project links

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Latest release

3 maintainers

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Credits

Author: The pyPESTO developers

Maintainer: Paul Jonas Jost

GitHub Statistics

Data verified by PyPI on Sep 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.11

Provides Extra

amici petab ipopt dlib nlopt pyswarm cma pyswarms fides mpi pymc jax emcee dynesty mltools julia select test roadrunner all-optimizers example doc all

Tags

parameter inference optimization sampling profiles ODE AMICI systems biology

Classifiers

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