AMICI - Scalable numerical simulation and sensitivity analysis of dynamical systems

• Applicant: Professor Dr.-Ing. Jan Hasenauer
• Subject Area: Bioinformatics and Theoretical Biology ; Software Engineering and Programming Languages
• Term: from 2021 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 443187771

• Final Report Year: 2025

Final Report Abstract

Ordinary differential equation (ODE) and differential algebraic equation (DAE) models are important tools in life sciences, engineering and many other research fields. They allow for the integrative analysis of heterogeneous data to further the understanding of dynamical systems. However, the simulation and parameterization of ODE and DAE models requires tailored and easy-to-use tools. To support parameterization of models of ever increasing size, scalability and performance are essential requirements. To this end, we developed the o research software AMICI (Advanced Multi-language Interface to CVODES and IDAS) which allows for the efficient and scalable simulation of such models. AMICI builds upon the well-established SUNDIALS solver C library, to which it provides an easy-to-use high-level interface as well as a wide array of additional features relevant to systems biologists as well as researchers of related fields. The aim of this project was to make AMICI more accessible, fitter for reuse, extend its functionality, and improve its overall quality. During the course of this project, AMICI’s functionality, documentation, and test suite have been significantly extended. Support for the community standards SBML and PEtab has been greatly improved. Two workshops have been organized to foster the exchange between AMICI users and developers. A total of 30 versions have been released over the duration of this project. The number of scientific publications using AMICI has increased from 30 by the time of the project application, to 94 by the end of the project, underlining AMICI’s persisting relevance.

Publications

• A More Expressive Spline Representation for SBML Models Improves Code Generation Performance in AMICI. Lecture Notes in Computer Science, 36-43. Springer Nature Switzerland.
  Contento, Lorenzo; Stapor, Paul; Weindl, Daniel & Hasenauer, Jan
  
• Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks. PLOS Computational Biology, 19(1), e1010783.
  Lakrisenko, Polina; Stapor, Paul; Grein, Stephan; Paszkowski, Łukasz; Pathirana, Dilan; Fröhlich, Fabian; Lines, Glenn Terje; Weindl, Daniel & Hasenauer, Jan
  
• Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes. iScience, 26(11), 108083.
  Raimúndez, Elba; Fedders, Michael & Hasenauer, Jan
  
• pyPESTO: a modular and scalable tool for parameter estimation for dynamic models. Bioinformatics, 39(11).
  Schälte, Yannik; Fröhlich, Fabian; Jost, Paul J.; Vanhoefer, Jakob; Pathirana, Dilan; Stapor, Paul; Lakrisenko, Polina; Wang, Dantong; Raimúndez, Elba; Merkt, Simon; Schmiester, Leonard; Städter, Philipp; Grein, Stephan; Dudkin, Erika; Doresic, Domagoj; Weindl, Daniel & Hasenauer, Jan
  
• Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states. PLOS ONE, 19(10), e0312148.
  Lakrisenko, Polina; Pathirana, Dilan; Weindl, Daniel & Hasenauer, Jan
  
• Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data. Bioinformatics, 40(Supplement_1), i558-i566.
  Dorešić, Domagoj; Grein, Stephan & Hasenauer, Jan