Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.
Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.
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DOI：
10.1371/journal.pone.0136139
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Marathe M
中科院分区：
文献类型：
--
作者：
Alam M;Deng X;Philipson C;Bassaganya-Riera J;Bisset K;Carbo A;Eubank S;Hontecillas R;Hoops S;Mei Y;Abedi V;Marathe M
基于代理的模型（ABM）被广泛用于研究免疫系统，提供了底层系统的过程和交互式视图。组件的交互和单个对象的行为在程序上被描述为内部状态和局部交互的函数，这些交互通常是随机的。这种模型通常具有复杂的结构，并且由大量的建模参数组成。确定控制系统结果的关键建模参数是非常具有挑战性的。灵敏度分析在量化大规模相互作用系统（包括大型复杂反弹道导弹）中建模参数的影响方面起着至关重要的作用。执行模拟的高计算成本阻碍了使用详尽的参数设置运行实验。分析这种复杂系统的现有技术通常集中在局部灵敏度分析上，即一次一个参数，或者特定参数设置的紧密“邻域”。然而，这些方法不足以准确地测量参数的不确定性和敏感性，因为它们忽略了参数对系统的全局影响。在这篇文章中，我们开发了新的实验设计和分析技术，进行全球和本地的大规模ABM的灵敏度分析。所提出的方法可以有效地识别最重要的参数，并量化它们对系统结果的贡献。我们证明了肠道免疫模拟器（ENISI），一个大规模的ABM环境，使用的计算模型的免疫反应幽门螺杆菌定植的胃粘膜的拟议方法。
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.