一个高效和灵活的框架来推断基于代理的模型参数的全球敏感性
Daniel R Bergman1,2,3, Trachette Jackson1, Harsh Vardhan Jain4
1Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS computational biology
|September 8, 2025
概括
我们开发了SMoRe GloS,这是一种快速而准确的方法,用于在基于代理的模型 (ABM) 中进行全球灵敏度分析. 这种方法提高了复杂系统的不确定性量化,提高了模型预测的可靠性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生态建模 生态建模
背景情况:
- 基于代理的模型 (ABM) 模拟复杂的系统,但它们的预测需要通过全球灵敏度分析量化不确定性.
- 现有的全球灵敏度方法在计算上昂贵,限制了它们的应用到复杂的ABM.
研究的目的:
- 介绍SMoRe GloS (用于总结全球敏感性的替代建模),这是ABM全球敏感性分析的计算效率高的方法.
- 能够对复杂的ABM进行准确的不确定性量化和参数空间探索.
主要方法:
- 利用明确制定的替代模型来近似ABM行为.
- 将SMoRe GloS应用于体外细胞增殖和3D血管瘤生长的ABMs.
- 将SMoRe GloS的性能与莫里斯和eFAST方法进行比较.
主要成果:
- SMoRe GloS实现了大幅加快速度,在几分钟内完成分析,而eFAST则需要几天.
- 该方法准确地回收了简单和复杂的生物ABM的全球灵敏度指数.
- SMoRe GloS估计了替代模型中没有明确的参数的灵敏度.
结论:
- 在ABM中,SMoRe GloS为全球敏感性分析提供了一个计算效率高,准确的解决方案.
- 这种方法提高了复杂系统的不确定性量化和模型可靠性.
- SMoRe GloS促进了对模型行为的更深入的探索,并增加了对预测的信心.
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