AMICI：大型常微分方程模型的高性能灵敏度分析
AMICI: high-performance sensitivity analysis for large ordinary differential equation models.
作者信息
Fröhlich Fabian, Weindl Daniel, Schälte Yannik, Pathirana Dilan, Paszkowski Łukasz, Lines Glenn Terje, Stapor Paul, Hasenauer Jan
机构信息
Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany.
出版信息
Bioinformatics. 2021 Oct 25;37(20):3676-3677. doi: 10.1093/bioinformatics/btab227.
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.
摘要
常微分方程模型有助于理解细胞信号转导和其他生物过程。然而，对于大型综合模型，模拟或校准的计算成本可能会受到限制。AMICI是一个用C++/Python/MATLAB实现的模块化工具箱，它提供了高效的模拟和灵敏度分析例程，专门用于基于梯度的可扩展参数估计和不确定性量化。
可用性和实现方式
AMICI根据宽松的BSD-3-Clause许可发布，源代码可在https://github.com/AMICI-dev/AMICI上公开获取。可引用的版本存档于Zenodo。
补充信息
补充数据可在《生物信息学》在线获取。
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