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AMICI: high-performance sensitivity analysis for large ordinary differential equation models.

Bioinformatics (Oxford, England)

Authors: Fabian Fröhlich, Daniel Weindl, Yannik Schälte, Dilan Pathirana, Łukasz Paszkowski, Glenn Terje Lines, Paul Stapor, Jan Hasenauer

SUMMARY: 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.

AVAILABILITYAND 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.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

© The Author(s) 2021. Published by Oxford University Press.

PMID: 33821950

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