Multiscale computational models of complex biological systems.

Multiscale computational models of complex biological systems.
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DOI:
10.1146/annurev-bioeng-071811-150104
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发表时间:
2013
影响因子:
9.7
通讯作者:
Peirce SM
Peirce SM
中科院分区:
工程技术1区
文献类型:
--
作者:
Walpole J;Papin JA;Peirce SM

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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平台的定量数据,使得多尺度建模得以扩展,成为以实验相关方式更全面地研究生物现象的手段。本综述旨在重点介绍最近发表的生物系统多尺度模型,同时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。此外,我们建议如何最好地利用这些多尺度模型,使用定量的生物医学工程方法以非直观的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。
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 platforms, 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 while 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 best leverage these multiscale models to gain insight into biological systems using quantitative, biomedical engineering methods to analyze data in non-intuitive ways. These topics are discussed with a focus on the future of the field, the current challenges encountered, and opportunities yet to be realized.
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