Awesome
pyPESTO - Parameter EStimation TOolbox for python
<img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo_wordmark.png" width="50%" alt="pyPESTO logo"/>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
- Uncertainty quantification using various methods:
- 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/:
<p align="center"> <img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/main/doc/logo/logo.png" height="75" alt="pyPESTO Logo"> </p>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.