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

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++/...

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Detalles Bibliográficos
Autores principales: Fröhlich, Fabian, Weindl, Daniel, Schälte, Yannik, Pathirana, Dilan, Paszkowski, Łukasz, Lines, Glenn Terje, Stapor, Paul, Hasenauer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545331/
https://www.ncbi.nlm.nih.gov/pubmed/33821950
http://dx.doi.org/10.1093/bioinformatics/btab227
Descripción
Sumario: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.