https://github.com/ICB-DCM/pyPESTO
python Parameter EStimation TOolbox
https://github.com/ICB-DCM/pyPESTO
hacktoberfest optimization parameter-estimation python systems-biology
Last synced: 6 months ago
JSON representation
python Parameter EStimation TOolbox
- Host: GitHub
- URL: https://github.com/ICB-DCM/pyPESTO
- Owner: ICB-DCM
- License: bsd-3-clause
- Created: 2018-07-25T15:51:29.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-10-17T08:49:17.000Z (7 months ago)
- Last Synced: 2024-10-17T08:56:33.366Z (7 months ago)
- Topics: hacktoberfest, optimization, parameter-estimation, python, systems-biology
- Language: Python
- Homepage: https://pypesto.readthedocs.io
- Size: 45 MB
- Stars: 217
- Watchers: 11
- Forks: 47
- Open Issues: 132
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
# pyPESTO - Parameter EStimation TOolbox for python
**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:
## 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
## 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):
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.