# 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.
[](https://badge.fury.io/py/pypesto)
[](https://github.com/ICB-DCM/pyPESTO/actions)
[](https://codecov.io/gh/ICB-DCM/pyPESTO)
[](https://pypesto.readthedocs.io)
[](https://doi.org/10.5281/zenodo.2553546)
## Feature overview
*Feature overview of pyPESTO. Figure taken from the [Bioinformatics publication](https://doi.org/10.1093/bioinformatics/btad711).*
pyPESTO features include:
* Parameter estimation interfacing **multiple optimization algorithms** including
multi-start local and global optimization. ([example](https://pypesto.readthedocs.io/en/latest/example/getting_started.html),
[overview of optimizers](https://pypesto.readthedocs.io/en/latest/api/pypesto.optimize.html))
* Interface to **multiple simulators** including
* [AMICI](https://github.com/AMICI-dev/AMICI/) for efficient simulation and
sensitivity analysis of ordinary differential equation (ODE) models. ([example](https://pypesto.readthedocs.io/en/latest/example/amici.html))
* [RoadRunner](https://libroadrunner.org/) for simulation of SBML models. ([example](https://pypesto.readthedocs.io/en/latest/example/roadrunner.html))
* [Jax](https://jax.readthedocs.io/en/latest/quickstart.html) and
[Julia](https://julialang.org) 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](https://emcee.readthedocs.io/en/stable/),
[pymc](https://www.pymc.io/welcome.html) and
[dynesty](https://dynesty.readthedocs.io/en/stable/).
([example](https://pypesto.readthedocs.io/en/latest/example/sampler_study.html))
* **Variational inference**
* **Complete** parameter estimation **pipeline** for systems biology problems specified in
[SBML](http://sbml.org/) and [PEtab](https://github.com/PEtab-dev/PEtab).
([example](https://pypesto.readthedocs.io/en/latest/example/petab_import.html))
* Parameter estimation pipelines for **different modes of data**:
* **Relative (scaled and offset) data** as described in
[Schmiester et al. (2020)](https://doi.org/10.1093/bioinformatics/btz581).
([example](https://pypesto.readthedocs.io/en/latest/example/relative_data.html))
* **Ordinal data** as described in
[Schmiester et al. (2020)](https://doi.org/10.1007/s00285-020-01522-w) and
[Schmiester et al. (2021)](https://doi.org/10.1093/bioinformatics/btab512).
([example](https://pypesto.readthedocs.io/en/latest/example/ordinal_data.html))
* **Censored data**. ([example](https://pypesto.readthedocs.io/en/latest/example/censored_data.html))
* **Semiquantitative data** as described in [Doresic et al. (2024)](https://doi.org/10.1093/bioinformatics/btae210). ([example](https://pypesto.readthedocs.io/en/latest/example/semiquantitative_data.html))
* **Model selection**. ([example](https://pypesto.readthedocs.io/en/latest/example/model_selection.html))
* Various **visualization methods** to analyze parameter estimation results.
## Quick install
The simplest way to install **pyPESTO** is via pip:
```shell
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](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:**
[](https://doi.org/10.5281/zenodo.2553546)
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](https://doi.org/10.1093/bioinformatics/btad711)
When presenting work that employs pyPESTO, feel free to use one of the icons in
[doc/logo/](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](https://pypesto.readthedocs.io/en/latest/references.html).
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**](https://github.com/ICB-DCM/PESTO/) a parameter estimation
toolbox for MATLAB, whose development is discontinued.
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([example](https://pypesto.readthedocs.io/en/latest/example/model_selection.html))\n* Various **visualization methods** to analyze parameter estimation results.\n\n## Quick install\n\nThe simplest way to install **pyPESTO** is via pip:\n\n```shell\npip3 install pypesto\n```\n\nMore information is available here:\nhttps://pypesto.readthedocs.io/en/latest/install.html\n\n## Documentation\n\nThe documentation is hosted on readthedocs.io:\n<https://pypesto.readthedocs.io>\n\n## Examples\n\nMultiple use cases are discussed in the documentation. In particular, there are\njupyter notebooks in the [doc/example](doc/example) directory.\n\n## Contributing\n\nWe are happy about any contributions. For more information on how to contribute\nto pyPESTO check out\n<https://pypesto.readthedocs.io/en/latest/contribute.html>\n\n## How to Cite\n\n**Citeable DOI for the latest pyPESTO release:**\n[](https://doi.org/10.5281/zenodo.2553546)\n\nWhen using pyPESTO in your project, please cite\n* Sch\u00e4lte, Y., Fr\u00f6hlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,\n Lakrisenko, P., Wang, D., Raim\u00fandez, E., Merkt, S., Schmiester, L., St\u00e4dter, P.,\n Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. 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