AMICI: high-performance sensitivity analysis for large ordinary differential equation models. Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Availability and implementation: AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte, Jan Hasenauer: yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML (2021) not zbMATH
- Pedretscher, B.; Kaltenbacher, B.; Pfeiler, O.: Parameter identification and uncertainty quantification in stochastic state space models and its application to texture analysis (2019)
Further publications can be found at: https://amici.readthedocs.io/en/latest/references.html