论文标题
Amici：大型普通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
论文作者
论文摘要
普通的微分方程模型有助于理解细胞信号转导和其他生物学过程。但是，对于大型而全面的模型，模拟或校准的计算成本可能是限制的。 AMICI是一种在C ++/Python/MatLab中实现的模块化工具箱，可提供有效的仿真和灵敏度分析例程，该例程是针对可扩展的，基于梯度的参数估计和不确定性定量定量的。 AMICI在允许的BSD-3-CLAUSE许可下发布，并在https://github.com/amici-dev/amici上公开获得源代码。可提及的版本在Zenodo上存档。
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.
