Renardy Marissa, Hult Caitlin, Evans Stephanie, Linderman Jennifer J, Kirschner Denise E
University of Michigan Medical School, Department of Microbiology and Immunology.
University of Michigan, Department of Chemical Engineering.
Curr Opin Biomed Eng. 2019 Sep;11:109-116. doi: 10.1016/j.cobme.2019.09.012. Epub 2019 Oct 15.
Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.
生物系统的数学模型需要既能反映又能管理生物现象固有的复杂性。多尺度模型凭借其通用性以及在多个尺度上捕捉行为的能力，提供了一种有价值的方法。由于多尺度模型通常具有非线性和随机性，以及参数值未知，存在各种类型的不确定性；因此，通过敏感性分析对这种不确定性进行有效评估和量化很重要。在本综述中，我们在多尺度和多隔室模型的背景下讨论全局敏感性分析，并强调其在模型开发和分析中的价值。我们概述了敏感性分析方法、将此类方法扩展到多尺度设置的方法，以及敏感性分析如何为模型简化提供信息的示例。通过示意图和对以往工作的引用，我们旨在强调此类技术的优势和实用性。
