Journal
BIOINFORMATICS
Volume 37, Issue 20, Pages 3676-3677Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab227
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Funding
- European Union [686282]
- Federal Ministry of Education and Research of Germany [01ZX1916A, 01ZX1705A, 031L0159C]
- German Research Foundation [HA7376/1-1, EXC-2047/1-390685813]
- Human Frontier Science Program [LT000259/2019-L1]
- National Institute of Health [U54CA225088]
- Federal Ministry of Economic Affairs and Energy [16KN074236]
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Ordinary differential equation models are useful for understanding biological processes like cellular signal transduction, but computational costs can be limiting for large models. AMICI is a modular toolbox implemented in multiple languages that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.
A 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.
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