AMICI: high-performance sensitivity analysis for large ordinary differential equation models
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...
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Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2021-10, Vol.37 (20), p.3676-3677 |
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Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 1367-4803 1367-4811 |
DOI: | 10.1093/bioinformatics/btab227 |