pypesto 0.5.4

python-based Parameter EStimation TOolbox

pyPESTO - Parameter EStimation TOolbox for python

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

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:
  • Relative (scaled and offset) data as described in Schmiester et al. (2020). (example)
  • Ordinal data as described in Schmiester et al. (2020) and Schmiester et al. (2021). (example)
  • Censored data. (example)
  • Semiquantitative data as described in Doresic et al. (2024). (example)
• 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:

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.

References

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