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Fröhlich, F.* ; Weindl, D. ; Schälte, Y. ; Pathirana, D.* ; Paszkowski, L.* ; Lines, G.T.* ; Stapor, P. ; Hasenauer, J.

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

Bioinformatics 37, 3676-3677 (2021)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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. AVAILABILITY: 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 37, Heft: 20, Seiten: 3676-3677 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Federal Ministry of Economic Affairs and Energy
National Institute of Health
Human Frontier Science Program
German Research Foundation
Federal Ministry of Education and Research of Germany
European Union