Package: amici
Advanced Multilanguage Interface for CVODES and IDASAboutAMICI provides a multi-language (Python, C++, Matlab) interface for theSUNDIALSsolversCVODES(for ordinary differential equations) andIDAS(for algebraic differential equations). AMICI allows the user to read
differential equation models specified asSBMLorPySBand automatically compiles such models into Python modules, C++ libraries or
Matlab.mexsimulation files.In contrast to the (no longer maintained)sundialsTBMatlab interface, all necessary functions are transformed into native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 statusFeaturesSBML importPySB importGeneration of C++ code for model simulation and sensitivity
computationAccess to and high customizability of CVODES and IDAS solverPython, C++, Matlab interfaceSensitivity analysisforwardsteady stateadjointfirst- and second-orderPre-equilibration and pre-simulation conditionsSupport fordiscrete events and logical operationsInterfaces & workflowThe AMICI workflow starts with importing a model from eitherSBML(Matlab, Python),PySB(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.mexfile and
is then used for model simulation.Getting startedThe AMICI source code is available athttps://github.com/AMICI-dev/AMICI/.
To install AMICI, first read the installation instructions forPython,C++orMatlab.
There are also instructions for using AMICI insidecontainers.To get you started with Python-AMICI, the best way might be checking out thisJupyter notebook.To get started with Matlab-AMICI, various examples are available
inmatlab/examples/.Comprehensive documentation is available athttps://amici.readthedocs.io/en/latest/.Anycontributionsto AMICI are welcome (code, bug reports, suggestions for improvements, ...).Getting helpIn case of questions or problems with using AMICI, feel free to post anissueon GitHub. We are trying to
get back to you quickly.Projects using AMICIThere are several tools for parameter estimation offering good integration
with AMICI:pyPESTO: Python library for
optimization, sampling and uncertainty analysispyABC: Python library for
parallel and scalable ABC-SMC (Approximate Bayesian Computation - Sequential
Monte Carlo)parPE: C++ library for parameter
estimation of ODE models offering distributed memory parallelism with focus
on problems with many simulation conditions.PublicationsCiteable DOI for the latest AMICI release:There is a list ofpublications using AMICI.
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 citeFröhlich, 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.@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 indocumentation/gfx/,
which are available under aCC0license: