{"id":13699297,"url":"https://github.com/AMICI-dev/AMICI","last_synced_at":"2025-05-04T16:33:16.257Z","repository":{"id":37449260,"uuid":"43677177","full_name":"AMICI-dev/AMICI","owner":"AMICI-dev","description":"Advanced Multilanguage Interface to CVODES and IDAS","archived":false,"fork":false,"pushed_at":"2024-10-19T13:37:06.000Z","size":220819,"stargazers_count":108,"open_issues_count":119,"forks_count":31,"subscribers_count":11,"default_branch":"master","last_synced_at":"2024-10-19T14:36:17.272Z","etag":null,"topics":["adjoint-sensitivities","cvode","cvodes","differentialequations","forward-sensitivities","hacktoberfest","idas","kinetic-modeling","mechanistic-models","modeling","ode","parameter-estimation","petab","pysb","python","sbml","sensitivities","sensitivity-analysis","simulation","systemsbiology"],"latest_commit_sha":null,"homepage":"https://amici.readthedocs.io/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AMICI-dev.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2015-10-05T10:08:05.000Z","updated_at":"2024-10-03T16:23:33.000Z","dependencies_parsed_at":"2023-10-20T16:48:38.335Z","dependency_job_id":"4f1ab85e-117a-4f7f-aa34-f74feb2a5418","html_url":"https://github.com/AMICI-dev/AMICI","commit_stats":{"total_commits":3005,"total_committers":30,"mean_commits":"100.16666666666667","dds":0.6189683860232945,"last_synced_commit":"1a99308c23a40a4175aa6b31f01c131f3ee4876e"},"previous_names":[],"tags_count":106,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AMICI-dev%2FAMICI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AMICI-dev%2FAMICI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AMICI-dev%2FAMICI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AMICI-dev%2FAMICI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AMICI-dev","download_url":"https://codeload.github.com/AMICI-dev/AMICI/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224092104,"owners_count":17254152,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adjoint-sensitivities","cvode","cvodes","differentialequations","forward-sensitivities","hacktoberfest","idas","kinetic-modeling","mechanistic-models","modeling","ode","parameter-estimation","petab","pysb","python","sbml","sensitivities","sensitivity-analysis","simulation","systemsbiology"],"created_at":"2024-08-02T20:00:30.007Z","updated_at":"2025-05-04T16:33:16.247Z","avatar_url":"https://github.com/AMICI-dev.png","language":"Jupyter Notebook","readme":"\u003cimg src=\"https://raw.githubusercontent.com/AMICI-dev/AMICI/master/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 multi-language (Python, C++, Matlab) 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, C++ libraries or\nMatlab `.mex` simulation files.\n\nIn contrast to the (no longer maintained)\n[sundialsTB](https://computing.llnl.gov/projects/sundials/sundials-software)\nMatlab interface, all necessary functions are transformed into native\nC++ code, which allows for a significantly faster simulation.\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/master/graph/badge.svg\" alt=\"Code coverage\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sonarcloud.io/dashboard?id=ICB-DCM_AMICI\u0026branch=master\"\u003e\n  \u003cimg src=\"https://sonarcloud.io/api/project_badges/measure?branch=master\u0026project=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, C++, Matlab 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/) (Matlab, Python), [PySB](http://pysb.org/) (Python),\nor a Matlab definition of the model (Matlab-only). From this input,\nall equations for model simulation\nare derived symbolically and C++ code is generated. This code is then\ncompiled into a C++ library, a Python module, or a Matlab `.mex` file and\nis then used for model simulation.\n\n![AMICI workflow](https://raw.githubusercontent.com/AMICI-dev/AMICI/master/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),\n[C++](https://amici.readthedocs.io/en/develop/cpp_installation.html) or\n[Matlab](https://amici.readthedocs.io/en/develop/matlab_installation.html).\nThere are also instructions for using AMICI inside\n[containers](https://github.com/AMICI-dev/AMICI/tree/master/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/master/doc/examples/getting_started/GettingStarted.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/develop?labpath=doc%2Fexamples%2Fgetting_started%2FGettingStarted.ipynb).\n\nTo get started with Matlab-AMICI, various examples are available\nin [matlab/examples/](https://github.com/AMICI-dev/AMICI/tree/master/matlab/examples).\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/develop/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/master/doc/gfx),\nwhich are available under a\n[CC0](https://github.com/AMICI-dev/AMICI/tree/master/doc/gfx/LICENSE.md)\nlicense:\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/AMICI-dev/AMICI/master/doc/gfx/logo_text.png\" height=\"75\" alt=\"AMICI Logo\"\u003e\n\u003c/p\u003e\n","funding_links":[],"categories":["\u003cspan id=\"head11\"\u003e3.1. Differentiation, Quadrature and Tensor computation\u003c/span\u003e"],"sub_categories":["\u003cspan id=\"head12\"\u003e3.1.1. Auto Differentiation\u003c/span\u003e"],"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"}