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Figure taken from the [Bioinformatics publication](https://doi.org/10.1093/bioinformatics/btad711).*\n\npyPESTO features include:\n\n* Parameter estimation interfacing **multiple optimization algorithms** including\n  multi-start local and global optimization. ([example](https://pypesto.readthedocs.io/en/latest/example/getting_started.html),\n  [overview of optimizers](https://pypesto.readthedocs.io/en/latest/api/pypesto.optimize.html))\n* Interface to **multiple simulators** including\n  * [AMICI](https://github.com/AMICI-dev/AMICI/) for efficient simulation and\n    sensitivity analysis of ordinary differential equation (ODE) models. ([example](https://pypesto.readthedocs.io/en/latest/example/amici.html))\n  * [RoadRunner](https://libroadrunner.org/) for simulation of SBML models. ([example](https://pypesto.readthedocs.io/en/latest/example/roadrunner.html))\n  * [Jax](https://jax.readthedocs.io/en/latest/quickstart.html) and\n    [Julia](https://julialang.org) for automatic differentiation.\n* **Uncertainty quantification** using various methods:\n  * **Profile likelihoods**.\n  * **Sampling** using Markov chain Monte Carlo (MCMC), parallel tempering, and\n    interfacing other samplers including [emcee](https://emcee.readthedocs.io/en/stable/),\n    [pymc](https://www.pymc.io/welcome.html) and\n    [dynesty](https://dynesty.readthedocs.io/en/stable/).\n    ([example](https://pypesto.readthedocs.io/en/latest/example/sampler_study.html))\n  * **Variational inference**\n* **Complete** parameter estimation **pipeline** for systems biology problems specified in\n  [SBML](http://sbml.org/) and [PEtab](https://github.com/PEtab-dev/PEtab).\n  ([example](https://pypesto.readthedocs.io/en/latest/example/petab_import.html))\n* Parameter estimation pipelines for **different modes of data**:\n  * **Relative (scaled and offset) data** as described in\n    [Schmiester et al. (2020)](https://doi.org/10.1093/bioinformatics/btz581).\n    ([example](https://pypesto.readthedocs.io/en/latest/example/relative_data.html))\n  * **Ordinal data** as described in\n    [Schmiester et al. (2020)](https://doi.org/10.1007/s00285-020-01522-w) and\n    [Schmiester et al. (2021)](https://doi.org/10.1093/bioinformatics/btab512).\n    ([example](https://pypesto.readthedocs.io/en/latest/example/ordinal_data.html))\n  * **Censored data**. ([example](https://pypesto.readthedocs.io/en/latest/example/censored_data.html))\n  * **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))\n* **Model selection**. ([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\u003chttps://pypesto.readthedocs.io\u003e\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\u003chttps://pypesto.readthedocs.io/en/latest/contribute.html\u003e\n\n## How to Cite\n\n**Citeable DOI for the latest pyPESTO release:**\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2553546.svg)](https://doi.org/10.5281/zenodo.2553546)\n\nWhen using pyPESTO in your project, please cite\n* Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,\n  Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P.,\n  Grein, S., Dudkin, E., Doresic, D., Weindl, D., \u0026 Hasenauer, J. (2023). pyPESTO: A\n  modular and scalable tool for parameter estimation for dynamic models,\n  Bioinformatics, 2023, btad711, [doi:10.1093/bioinformatics/btad711](https://doi.org/10.1093/bioinformatics/btad711)\n\nWhen presenting work that employs pyPESTO, feel free to use one of the icons in\n[doc/logo/](doc/logo):\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/ICB-DCM/pyPESTO/main/doc/logo/logo.png\" height=\"75\" alt=\"pyPESTO Logo\"\u003e\n\u003c/p\u003e\n\nThere is a list of [publications using pyPESTO](https://pypesto.readthedocs.io/en/latest/references.html).\nIf you used pyPESTO in your work, we are happy to include\nyour project, please let us know via a GitHub issue.\n\n## References\n\npyPESTO supersedes [**PESTO**](https://github.com/ICB-DCM/PESTO/) a parameter estimation\ntoolbox for MATLAB, whose development is discontinued.\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FICB-DCM%2FpyPESTO","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FICB-DCM%2FpyPESTO","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FICB-DCM%2FpyPESTO/lists"}