标题： SUND：使用非线性动态模型进行仿真 - 用于以模块化方式模拟多级、时间动态系统的工具箱 显示英文标题

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

摘要： 在对复杂、分层和时变系统进行建模时，例如生物系统，良好的计算工具是必不可少的。 当前的工具虽然功能强大，但在模块化模型组合、分层系统构建和时间依赖输入处理方面往往缺乏全面的框架，尤其是在Python生态系统中。 我们提出了SUND（使用非线性动态模型进行仿真），这是一个旨在解决这些挑战的Python工具箱。 SUND为定义、组合和模拟多级时变系统提供了一个统一的框架。 该工具箱使用户能够定义具有可连接输入和输出的模型，从而促进从简单、可重用组件构建复杂系统。 它支持时间依赖函数和分段常数输入，能够直观地模拟各种实验条件，如多种给药方案。 我们通过模拟一个多级人体葡萄糖-胰岛素系统模型来展示该工具箱的功能，展示了其在处理多个时间尺度和生物细节层次方面的灵活性。 SUND是开源的，易于扩展，并可在PyPI（https://pypi.org/project/sund/）和Gitlab（https://gitlab.liu.se/ISBgroup/projects/sund/）上获得。 显示英文摘要

摘要： 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/).