Dataset Information
AMICI: high-performance sensitivity analysis for large ordinary differential equation models.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Frohlich F
PROVIDER: S-EPMC8545331 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
AMICI: high-performance sensitivity analysis for large ordinary differential equation models.
Bioinformatics (Oxford, England) 20211001 20
<h4>Summary</h4>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.<h4>Availabilityand ...[more]