Background

History

The development of AMICI started out in 2015 by Fabian Fröhlich in the Jan Hasenauer lab as an Advanced MATLAB Interface to CVODES and IDAS. The goal was to provide a fast and flexible interface to the SUNDIALS CVODES and IDAS solvers for the efficient simulation and sensitivity analysis of large-scale biochemical reaction networks while supporting high-level model descriptions in SBML as the de facto standard for systems biology models. In contrast to the (no longer maintained) sundialsTB MATLAB interface, all necessary functions were transformed into native C++ code, which allowed for significantly faster simulations.

Over time, various other researchers contributed to AMICI’s development. AMICI’s C core was reshaped into a reusable stand-alone C++ library, that was the basis for its Python interface. These additional interfaces lead to the reinterpretation of “AMICI” as “Advanced Multi-language Interface to CVODES and IDAS”.

Due to changes in MATLAB’s symbolic math library and to foster open and reusable research, the development of AMICI’s MATLAB interface was meanwhile discontinued, and the focus shifted to the Python interface.

Over the years, AMICI has evolved into a powerful and flexible tool for the simulation and sensitivity analysis of large-scale biochemical reaction networks, supporting different input formats, different types of sensitivity analysis, discrete events, and many other features. Meanwhile, the use of AMICI has been documented in around 100 research papers from different labs and companies.

AMICI is currently maintained by Fabian Fröhlich (@FFroehlich, Fröhlich lab) and Daniel Weindl (@dweindl, Hasenauer lab).

Publications on various features of AMICI

Some mathematical background for AMICI is provided in the following publications:

  • 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.

  • 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.

  • Terje Lines, Glenn, Łukasz Paszkowski, Leonard Schmiester, Daniel Weindl, Paul Stapor, and Jan Hasenauer. 2019. Efficient Computation of Steady States in Large-Scale ODE Models of Biochemical Reaction Networks. IFAC-PapersOnLine 52 (26): 32–37. DOI: 10.1016/j.ifacol.2019.12.232.

  • Stapor, Paul, Fabian Fröhlich, and Jan Hasenauer. 2018. Optimization and Profile Calculation of ODE Models Using Second Order Adjoint Sensitivity Analysis. Bioinformatics 34 (13): i151–i159. DOI: 10.1093/bioinformatics/bty230.

  • Lakrisenko, Polina, Paul Stapor, Stephan Grein, Łukasz Paszkowski, Dilan Pathirana, Fabian Fröhlich, Glenn Terje Lines, Daniel Weindl, and Jan Hasenauer. 2023. Efficient Computation of Adjoint Sensitivities at Steady-State in ODE Models of Biochemical Reaction Networks. PLoS Comput Biol 19(1): e1010783. DOI: 10.1371/journal.pcbi.1010783.

  • L. Contento, P. Stapor, D. Weindl, and J. Hasenauer. 2023. A more expressive spline representation for SBML models improves code generation performance in AMICI, In: Pang, J., Niehren, J. (eds) Computational Methods in Systems Biology. CMSB 2023. Lecture Notes in Computer Science, vol 14137. Springer, Cham. DOI: 10.1007/978-3-031-42697-1_3. Preprint available at bioRxiv.

  • Lakrisenko, Polina, Dilan Pathirana, Daniel Weindl, and Jan Hasenauer. 2024. Exploration of methods for computing sensitivities in ODE models at dynamic and steady states. arXiv:2405.16524 [q-bio.QM]. DOI: 10.48550/arXiv.2405.16524.

Note

Implementation details of the latest AMICI versions may differ from the ones given in the referenced manuscripts.

Third-Party numerical algorithms used by AMICI

AMICI uses the following packages from SUNDIALS:

  • CVODES:

    The sensitivity-enabled ODE solver in SUNDIALS. Radu Serban and Alan C. Hindmarsh. ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2005. PDF

  • IDAS

AMICI uses the following packages from SuiteSparse:

  • Algorithm 907: KLU A Direct Sparse Solver for Circuit Simulation Problems. Timothy A. Davis, Ekanathan Palamadai Natarajan, ACM Transactions on Mathematical Software, Vol 37, Issue 6, 2010, pp 36:1-36:17. PDF

  • Algorithm 837: AMD, an approximate minimum degree ordering algorithm, Patrick R. Amestoy, Timothy A. Davis, Iain S. Duff, ACM Transactions on Mathematical Software, Vol 30, Issue 3, 2004, pp 381-388. PDF

  • Algorithm 836: COLAMD, a column approximate minimum degree ordering algorithm, Timothy A. Davis, John R. Gilbert, Stefan I. Larimore, Esmond G. Ng ACM Transactions on Mathematical Software, Vol 30, Issue 3, 2004, pp 377-380. PDF

Others: