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src=\"https://raw.githubusercontent.com/AMICI-dev/AMICI/main/doc/gfx/banner.png\" height=\"60\" align=\"left\" alt=\"AMICI logo\"\u003e\n\n## Advanced Multilanguage Interface for CVODES and IDAS\n\n## About\n\nAMICI provides a Python and C++ interface for the\n[SUNDIALS](https://computing.llnl.gov/projects/sundials/) solvers\n[CVODES](https://computing.llnl.gov/projects/sundials/cvodes)\n(for ordinary differential equations) and\n[IDAS](https://computing.llnl.gov/projects/sundials/idas)\n(for algebraic differential equations). AMICI allows the user to read\ndifferential equation models specified as [SBML](http://sbml.org/)\nor [PySB](http://pysb.org/)\nand automatically compiles such models into Python modules or C++ libraries.\nThe generated model expressions along with the corresponding sensitivity\nequations are transformed into native C++ code which allows for a significantly\nfaster simulation.\n\n**NOTE: The former MATLAB interface has been removed in AMICI 1.0.**\n\nBeyond forward integration, the compiled simulation file also allows for\nforward sensitivity analysis, steady state sensitivity analysis and\nadjoint sensitivity analysis for likelihood-based output functions.\n\nThe interface was designed to provide routines for efficient gradient\ncomputation in parameter estimation of biochemical reaction models, but\nit is also applicable to a wider range of differential equation\nconstrained optimization problems.\n\n## Current build status\n\n\u003ca href=\"https://badge.fury.io/py/amici\"\u003e\n  \u003cimg src=\"https://badge.fury.io/py/amici.svg\" alt=\"PyPI version\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml\"\u003e\n  \u003cimg src=\"https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml/badge.svg\" alt=\"PyPI installation\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/AMICI-dev/AMICI\"\u003e\n  \u003cimg src=\"https://codecov.io/gh/AMICI-dev/AMICI/branch/main/graph/badge.svg\" alt=\"Code coverage\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sonarcloud.io/dashboard?id=ICB-DCM_AMICI\u0026branch=main\"\u003e\n  \u003cimg src=\"https://sonarcloud.io/api/project_badges/measure?project=ICB-DCM_AMICI\u0026metric=sqale_index\" alt=\"SonarCloud technical debt\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/43677177\"\u003e\n  \u003cimg src=\"https://zenodo.org/badge/43677177.svg\" alt=\"Zenodo DOI\"\u003e\u003c/a\u003e\n\u003ca href=\"https://amici.readthedocs.io/en/latest/?badge=latest\"\u003e\n \u003cimg src=\"https://readthedocs.org/projects/amici/badge/?version=latest\" alt=\"ReadTheDocs status\"\u003e\u003c/a\u003e\n\u003ca href=\"https://bestpractices.coreinfrastructure.org/projects/3780\"\u003e\n  \u003cimg src=\"https://bestpractices.coreinfrastructure.org/projects/3780/badge\" alt=\"coreinfrastructure bestpractices badge\"\u003e\u003c/a\u003e\n\n## Features\n\n* SBML import\n* PySB import\n* Generation of C++ code for model simulation and sensitivity\n  computation\n* Access to and high customizability of CVODES and IDAS solver\n* Python and C++ interface\n* Sensitivity analysis\n  * forward\n  * steady state\n  * adjoint\n  * first- and second-order\n* Pre-equilibration and pre-simulation conditions\n* Support for\n  [discrete events and logical operations](https://academic.oup.com/bioinformatics/article/33/7/1049/2769435)\n\n## Interfaces \u0026 workflow\n\nThe AMICI workflow starts with importing a model from either\n[SBML](http://sbml.org/) (Python) or [PySB](http://pysb.org/) (Python).\nFrom this input, all equations for model simulation\nare derived symbolically and C++ code is generated. This code is then\ncompiled into a plain C++ library or a Python module, and\nis then used for model simulation.\n\n![AMICI workflow](https://raw.githubusercontent.com/AMICI-dev/AMICI/main/doc/gfx/amici_workflow.png)\n\n## Getting started\n\nThe AMICI source code is available at https://github.com/AMICI-dev/AMICI/.\nTo install AMICI, first read the installation instructions for\n[Python](https://amici.readthedocs.io/en/latest/python_installation.html) or\n[C++](https://amici.readthedocs.io/en/latest/cpp_installation.html).\nThere are also instructions for using AMICI inside\n[containers](https://github.com/AMICI-dev/AMICI/tree/main/container).\n\nTo get you started with Python-AMICI, the best way might be checking out this\n[Jupyter notebook](https://github.com/AMICI-dev/AMICI/blob/main/doc/examples/getting_started/GettingStarted.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/main?labpath=doc%2Fexamples%2Fgetting_started%2FGettingStarted.ipynb).\n\nComprehensive documentation is available at\n[https://amici.readthedocs.io/en/latest/](https://amici.readthedocs.io/en/latest/).\n\nAny [contributions](https://amici.readthedocs.io/en/latest/CONTRIBUTING.html)\nto AMICI are welcome (code, bug reports, suggestions for improvements, ...).\n\n\n## Getting help\n\nIn case of questions or problems with using AMICI, feel free to post an\n[issue](https://github.com/AMICI-dev/AMICI/issues) on GitHub. We are trying to\nget back to you quickly.\n\n## Projects using AMICI\n\nThere are several tools for parameter estimation offering good integration\nwith AMICI:\n\n* [pyPESTO](https://github.com/ICB-DCM/pyPESTO): Python library for\n  optimization, sampling and uncertainty analysis\n* [pyABC](https://github.com/ICB-DCM/pyABC): Python library for\n  parallel and scalable ABC-SMC (Approximate Bayesian Computation - Sequential\n  Monte Carlo)\n* [parPE](https://github.com/ICB-DCM/parPE): C++ library for parameter\n  estimation of ODE models offering distributed memory parallelism with focus\n  on problems with many simulation conditions.\n\n## Publications\n\n**Citeable DOI for the latest AMICI release:**\n[![DOI](https://zenodo.org/badge/43677177.svg)](https://zenodo.org/badge/latestdoi/43677177)\n\nThere is a list of [publications using AMICI](https://amici.readthedocs.io/en/latest/references.html).\nIf you used AMICI in your work, we are happy to include\nyour project, please let us know via a GitHub issue.\n\nWhen using AMICI in your project, please cite:\n\n* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2021.\n  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,\n  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).\n\n  ```bibtex\n  @article{frohlich2020amici,\n    title={AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models},\n    author={Fr{\\\"o}hlich, Fabian and Weindl, Daniel and Sch{\\\"a}lte, Yannik and Pathirana, Dilan and Paszkowski, {\\L}ukasz and Lines, Glenn Terje and Stapor, Paul and Hasenauer, Jan},\n    journal = {Bioinformatics},\n    year = {2021},\n    month = {04},\n    issn = {1367-4803},\n    doi = {10.1093/bioinformatics/btab227},\n    note = {btab227},\n    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},\n  }\n  ```\n\nWhen presenting work that employs AMICI, feel free to use one of the icons in\n[doc/gfx/](https://github.com/AMICI-dev/AMICI/tree/main/doc/gfx),\nwhich are available under a\n[CC0](https://github.com/AMICI-dev/AMICI/tree/main/doc/gfx/LICENSE.md)\nlicense:\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/AMICI-dev/AMICI/main/doc/gfx/logo_text.png\" height=\"75\" alt=\"AMICI Logo\"\u003e\n\u003c/p\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famici-dev%2Famici","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famici-dev%2Famici","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famici-dev%2Famici/lists"}