amici 0.10.11

Advanced multi-language Interface to CVODES and IDAS (%s)

About AMICI

AMICI provides a multi-language (Python, C++, Matlab) interface for the SUNDIALS solvers CVODES (for ordinary differential equations) and IDAS (for algebraic differential equations). AMICI allows the user to read differential equation models specified as SBML and automatically compiles such models as .mex simulation files (Matlab), C++ executables or Python modules.

In contrast to the (no longer maintained) sundialsTB Matlab interface, all necessary functions are transformed into native C++ code, which allows for a significantly faster simulation.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.

Features

• SBML import (see details below)
• 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

Interfaces & workflow

The AMICI workflow starts with importing a model from either SBML (Matlab, 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.

Getting started

AMICI installation instructions are provided here.

To get you started with Python-AMICI the best way might be this Jupyter notebook.

For Matlab, various examples are available here.

Comprehensive documentation on installation and usage of AMICI is available online at http://icb-dcm.github.io/AMICI/.

Any contributions to AMICI are welcome, read more contributing here.

Getting help

In case of questions or problems with using AMICI, feel free to post an issue on Github. We are trying to get back to you quickly.

Publications

Citeable DOI for the latest AMICI release:

There is a list of publications 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 cite

• Fröhlich, F., Kaltenbacher, B., Theis, F. J., & Hasenauer, J. (2017). Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. Plos Computational Biology, 13(1), e1005331. doi: 10.1371/journal.pcbi.1005331 and/or
• Fröhlich, F., Theis, F. J., Rädler, J. O., & Hasenauer, J. (2017). Parameter estimation for dynamical systems with discrete events and logical operations. Bioinformatics, 33(7), 1049-1056. doi: 10.1093/bioinformatics/btw764

Status of SBML support in Python-AMICI

Python-AMICI currently passes 494 out of the 1780 (~28%) test cases from the semantic SBML Test Suite.

In additional, we currently plan to add support for the following features (see corresponding issues for details and progress):

• Events (currently Matlab-only)
• Rate rules
• Algebraic rules
• Species assignment rules
• Compartment assignment rules
• Models without species
• Logical operators

contributions are welcome.

However, the following features are unlikely to be supported:

• SBML extensions
• factorial(), ceil(), floor(), due to incompatibility with symbolic sensitivity computations
• initial assignments for parameters
• delay() due to missing SUNDIALS solver support

Current build status