Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental methodologies, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems, using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to leverage these multiscale models to gain insight into biological systems using quantitative biomedical engineering methods to analyze data in nonintuitive ways. These topics are discussed with a focus on the future of the field, current challenges encountered, and opportunities yet to be realized.

中文翻译：

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复杂生物系统的多尺度计算模型。

跨空间、时间和功能尺度的数据集成是生物医学工程工作的主要焦点。强大的计算平台的出现，加上来自高通量实验方法的定量数据，使得多尺度建模能够扩展为一种以实验相关的方式更全面地研究生物现象的手段。本综述旨在突出最近发表的生物系统多尺度模型，利用它们的成功为未来模型开发提出最佳实践。我们证明，通过选择适合任务的建模技术，耦合连续和离散系统可以最好地捕获跨空间尺度的生物信息。更远，我们建议如何利用这些多尺度模型使用定量生物医学工程方法以非直观方式分析数据来深入了解生物系统。这些主题的讨论重点是该领域的未来、当前遇到的挑战以及尚未实现的机遇。