SUND: simulation using nonlinear dynamic models - a toolbox for simulating multi-level, time-dynamic systems in a modular way

When modeling complex, hierarchical, and time-dynamic systems, such as biological systems, good computational tools are essential. Current tools, while powerful, often lack comprehensive frameworks for modular model composition, hierarchical system building, and time-dependent input handling, particularly within the Python ecosystem. We present SUND (Simulation Using Nonlinear Dynamic models), a Python toolbox designed to address these challenges. SUND provides a unified framework for defining, combining, and simulating multi-level time-dynamic systems. The toolbox enables users to define models with interconnectable inputs and outputs, facilitating the construction of complex systems from simpler, reusable components. It supports time-dependent functions and piecewise constant inputs, enabling intuitive simulation of various experimental conditions such as multiple dosing schemes. We demonstrate the toolbox's capabilities through simulation of a multi-level human glucose-insulin system model, showcasing its flexibility in handling multiple temporal scales, and levels of biological detail. SUND is open-source, easily extensible, and available at PyPI (https://pypi.org/project/sund/) and at Gitlab (https://gitlab.liu.se/ISBgroup/projects/sund/). ; 6 pages, 1 figure, software paper. The last two listed authors contributed equally to this work. Gunnar Cedersund is the corresponding author