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"info": {
"author": "Fabian Froehlich, Jan Hasenauer, Daniel Weindl and Paul Stapor",
"author_email": "fabian_froehlich@hms.harvard.edu",
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"classifiers": [
"Development Status :: 5 - Production/Stable",
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"License :: OSI Approved :: BSD License",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX :: Linux",
"Programming Language :: C++",
"Programming Language :: Python",
"Topic :: Scientific/Engineering :: Bio-Informatics"
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"description": "# About AMICI\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compiles such models into `.mex` simulation files\n(Matlab), C++ executables or Python modules.\n\nIn contrast to the (no longer maintained)\n[sundialsTB](https://computing.llnl.gov/projects/sundials/sundials-software)\nMatlab interface, all necessary functions are transformed into native\nC++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\n## Features\n\n* SBML import (see details below)\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 (Matlab-only)\n\n## Interfaces & workflow\n\nThe AMICI workflow starts with importing a model from either\n[SBML](http://sbml.org/) (Matlab, Python) or a Matlab definition of the\nmodel (Matlab-only). From this input, all 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\n\n## Getting started\n\nThe AMICI source code is available at https://github.com/ICB-DCM/AMICI/.\nTo install AMICI, first read the\n[installation instructions](http://icb-dcm.github.io/AMICI/md__i_n_s_t_a_l_l.html).\n\nTo get you started with Python-AMICI, the best way might be checking out this\n[Jupyter notebook](https://github.com/ICB-DCM/AMICI/blob/master/python/examples/example_steadystate/ExampleSteadystate.ipynb).\n\nTo get started with Matlab-AMICI, various examples are available\nin [matlab/examples/](https://github.com/ICB-DCM/AMICI/tree/master/matlab/examples).\n\nComprehensive documentation on installation and usage of AMICI is available\nonline at [http://icb-dcm.github.io/AMICI/](http://icb-dcm.github.io/AMICI/).\n\nAny [contributions](http://icb-dcm.github.io/AMICI/md__c_o_n_t_r_i_b_u_t_i_n_g.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/ICB-DCM/AMICI/issues) on Github. We are trying to\nget back to you quickly.\n\n## Publications\n\n**Citeable DOI for the latest AMICI release:**\n[](https://zenodo.org/badge/latestdoi/43677177)\n\nThere is a list of [publications using AMICI](documentation/references.md).\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* Fr\u00f6hlich, F., Kaltenbacher, B., Theis, F. J., & Hasenauer, J. (2017).\n Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.\n Plos Computational Biology, 13(1), e1005331.\n doi:[10.1371/journal.pcbi.1005331](https://doi.org/10.1371/journal.pcbi.1005331)\nand/or\n* Fr\u00f6hlich, F., Theis, F. J., R\u00e4dler, J. O., & Hasenauer, J. (2017).\n Parameter estimation for dynamical systems with discrete events and logical\n operations. Bioinformatics, 33(7), 1049-1056.\n doi:[10.1093/bioinformatics/btw764](https://doi.org/10.1093/bioinformatics/btw764)\n\n\n## Status of SBML support in Python-AMICI\n\nPython-AMICI currently passes 500 out of the 1780 (~28%) test cases from\nthe semantic\n[SBML Test Suite](https://github.com/sbmlteam/sbml-test-suite/)\n([current status](https://github.com/ICB-DCM/AMICI/actions)).\n\nIn addition, we currently plan to add support for the following features\n(see corresponding issues for details and progress):\n\n- Events (currently Matlab-only)\n- Rate rules\n- Algebraic rules\n- Species assignment rules\n- Compartment assignment rules\n- Models without species\n\ncontributions are welcome.\n\nHowever, the following features are unlikely to be supported:\n\n- SBML extensions\n- `factorial()`, `ceil()`, `floor()`, due to incompatibility with\n symbolic sensitivity computations\n- initial assignments for parameters\n- `delay()` due to missing SUNDIALS solver support\n\n\n## Current build status\n\n\n ![](\"https://badge.fury.io/py/amici.svg\")
\n\n ![](\"https://travis-ci.com/ICB-DCM/AMICI.svg?branch=master\")
\n\n ![](\"https://codecov.io/gh/ICB-DCM/AMICI/branch/master/graph/badge.svg\")
\n\n ![](\"https://api.codacy.com/project/badge/Grade/945235766e344a7fa36278feab915ff6\")
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