AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

TL;DR

AMICI is a high-performance, modular toolbox designed to efficiently perform simulation and sensitivity analysis on large ordinary differential equation models, enabling scalable biological data analysis.

Contribution

The paper introduces AMICI, a versatile toolbox that significantly improves computational efficiency for large ODE models in biological research.

Findings

AMICI enables faster simulation of large ODE models.

It provides scalable sensitivity analysis routines.

The toolbox supports gradient-based parameter estimation.

Abstract

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.