Background

This section is to be extended.

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. 2022. Efficient Computation of Adjoint Sensitivities at Steady-State in ODE Models of Biochemical Reaction Networks. bioRxiv 2022.08.08.503176. DOI: 10.1101/2022.08.08.503176.

  • L. Contento, P. Stapor, D. Weindl, and J. Hasenauer, “A more expressive spline representation for SBML models improves code generation performance in AMICI,” bioRxiv, 2023, DOI: 10.1101/2023.06.29.547120.

Note

Implementation details of the latest AMICI versions may differ from the ones given in the references 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: