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https://github.com/AMICI-dev/AMICI

Advanced Multilanguage Interface to CVODES and IDAS
https://github.com/AMICI-dev/AMICI

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

Last synced: about 1 month ago
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Advanced Multilanguage Interface to CVODES and IDAS

Host: GitHub
URL: https://github.com/AMICI-dev/AMICI
Owner: AMICI-dev
License: other
Created: 2015-10-05T10:08:05.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2024-10-19T13:37:06.000Z (about 2 months ago)
Last Synced: 2024-10-19T14:36:17.272Z (about 2 months ago)
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
Language: Jupyter Notebook
Homepage: https://amici.readthedocs.io/
Size: 211 MB
Stars: 108
Watchers: 11
Forks: 31
Open Issues: 119
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS

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awesome-sciml - AMICI-dev/AMICI: Advanced Multilanguage Interface to CVODES and IDAS

README

        

## Advanced Multilanguage Interface for CVODES and IDAS

## About

AMICI provides a multi-language (Python, C++, Matlab) interface for the

[SUNDIALS](https://computing.llnl.gov/projects/sundials/) solvers

[CVODES](https://computing.llnl.gov/projects/sundials/cvodes)

(for ordinary differential equations) and

[IDAS](https://computing.llnl.gov/projects/sundials/idas)

(for algebraic differential equations). AMICI allows the user to read

differential equation models specified as [SBML](http://sbml.org/)

or [PySB](http://pysb.org/)

and automatically compiles such models into Python modules, C++ libraries or

Matlab `.mex` simulation files.

In contrast to the (no longer maintained)

[sundialsTB](https://computing.llnl.gov/projects/sundials/sundials-software)

Matlab interface, all necessary functions are transformed into native

C++ code, which allows for a significantly faster simulation.

Beyond forward integration, the compiled simulation file also allows for

forward sensitivity analysis, steady state sensitivity analysis and

adjoint sensitivity analysis for likelihood-based output functions.

The interface was designed to provide routines for efficient gradient

computation in parameter estimation of biochemical reaction models, but

it is also applicable to a wider range of differential equation

constrained optimization problems.

## Current build status



  



  



  



  



  



 



  

## Features

* SBML import

* PySB import

* Generation of C++ code for model simulation and sensitivity

  computation

* Access to and high customizability of CVODES and IDAS solver

* Python, C++, Matlab interface

* Sensitivity analysis

  * forward

  * steady state

  * adjoint

  * first- and second-order

* Pre-equilibration and pre-simulation conditions

* Support for

  [discrete events and logical operations](https://academic.oup.com/bioinformatics/article/33/7/1049/2769435)

## Interfaces & workflow

The AMICI workflow starts with importing a model from either

[SBML](http://sbml.org/) (Matlab, Python), [PySB](http://pysb.org/) (Python),

or a Matlab definition of the model (Matlab-only). From this input,

all equations for model simulation

are derived symbolically and C++ code is generated. This code is then

compiled into a C++ library, a Python module, or a Matlab `.mex` file and

is then used for model simulation.

![AMICI workflow](https://raw.githubusercontent.com/AMICI-dev/AMICI/master/documentation/gfx/amici_workflow.png)

## Getting started

The AMICI source code is available at https://github.com/AMICI-dev/AMICI/.

To install AMICI, first read the installation instructions for

[Python](https://amici.readthedocs.io/en/latest/python_installation.html),

[C++](https://amici.readthedocs.io/en/develop/cpp_installation.html) or

[Matlab](https://amici.readthedocs.io/en/develop/matlab_installation.html).

There are also instructions for using AMICI inside

[containers](https://github.com/AMICI-dev/AMICI/tree/master/container).

To get you started with Python-AMICI, the best way might be checking out this

[Jupyter notebook](https://github.com/AMICI-dev/AMICI/blob/master/documentation/GettingStarted.ipynb)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/develop?labpath=documentation%2FGettingStarted.ipynb).

To get started with Matlab-AMICI, various examples are available

in [matlab/examples/](https://github.com/AMICI-dev/AMICI/tree/master/matlab/examples).

Comprehensive documentation is available at

[https://amici.readthedocs.io/en/latest/](https://amici.readthedocs.io/en/latest/).

Any [contributions](https://amici.readthedocs.io/en/develop/CONTRIBUTING.html)

to AMICI are welcome (code, bug reports, suggestions for improvements, ...).

## Getting help

In case of questions or problems with using AMICI, feel free to post an

[issue](https://github.com/AMICI-dev/AMICI/issues) on GitHub. We are trying to

get back to you quickly.

## Projects using AMICI

There are several tools for parameter estimation offering good integration

with AMICI:

* [pyPESTO](https://github.com/ICB-DCM/pyPESTO): Python library for

  optimization, sampling and uncertainty analysis

* [pyABC](https://github.com/ICB-DCM/pyABC): Python library for

  parallel and scalable ABC-SMC (Approximate Bayesian Computation - Sequential

  Monte Carlo)

* [parPE](https://github.com/ICB-DCM/parPE): C++ library for parameter

  estimation of ODE models offering distributed memory parallelism with focus

  on problems with many simulation conditions.

## Publications

**Citeable DOI for the latest AMICI release:**

[![DOI](https://zenodo.org/badge/43677177.svg)](https://zenodo.org/badge/latestdoi/43677177)

There is a list of [publications using AMICI](https://amici.readthedocs.io/en/latest/references.html).

If you used AMICI in your work, we are happy to include

your project, please let us know via a GitHub issue.

When using AMICI in your project, please cite

* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2021.

  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,

  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).

```

@article{frohlich2020amici,

  title={AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models},

  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},

  journal = {Bioinformatics},

  year = {2021},

  month = {04},

  issn = {1367-4803},

  doi = {10.1093/bioinformatics/btab227},

  note = {btab227},

  eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},

}

```

When presenting work that employs AMICI, feel free to use one of the icons in

[documentation/gfx/](https://github.com/AMICI-dev/AMICI/tree/master/documentation/gfx),

which are available under a

[CC0](https://github.com/AMICI-dev/AMICI/tree/master/documentation/gfx/LICENSE.md)

license: