AMICI: high-performance sensitivity analysis for large ordinary differential equation models.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
25 Oct 2021
25 Oct 2021
Historique:
received:
24
12
2020
revised:
18
03
2021
accepted:
01
04
2021
medline:
7
4
2021
pubmed:
7
4
2021
entrez:
6
4
2021
Statut:
ppublish
Résumé
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. 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 data are available at Bioinformatics online.
Identifiants
pubmed: 33821950
pii: 6209017
doi: 10.1093/bioinformatics/btab227
pmc: PMC8545331
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3676-3677Subventions
Organisme : NCI NIH HHS
ID : U54 CA225088
Pays : United States
Organisme : European Union's Horizon 2020 research and innovation program
ID : 686282
Organisme : Federal Ministry of Education and Research of Germany
ID : 01ZX1916A
Organisme : German Research Foundation
ID : HA7376/1-1
Organisme : Germany's Excellence Strategy
ID : EXC-2047/1-390685813
Organisme : Human Frontier Science Program
ID : LT000259/2019-L1
Organisme : National Institute of Health
ID : U54-CA225088
Organisme : Federal Ministry of Economic Affairs and Energy
ID : 16KN074236
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.