SMoRe GloS:一种高效灵活的框架,用于推断基于代理的模型参数的全球灵敏度
Daniel R Bergman1,2,3, Trachette Jackson1, Harsh Vardhan Jain4
1Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.
bioRxiv : the preprint server for biology
|September 30, 2024
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模拟的不确定性量化,提高了模型可靠性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 计算科学 计算科学
背景情况:
- 基于代理的模型 (ABM) 模拟复杂的系统,但需要强大的不确定性量化.
- 全球敏感性分析 (GSA) 评估了ABM的可靠性,但在计算上是昂贵的.
- 现有的GSA方法对于复杂,资源密集的ABM通常是不切实际的.
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的计算效率高的GSA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.
- 证明该方法的兼容性和性能与已建立的GSA技术相比.
主要方法:
- 开发了SMoRe GloS,使用明确制定的替代模型进行ABM分析.
- 将SMoRe GloS应用于2D细胞增殖试验和3D血管瘤生长模型.
- 将SMoRe GloS性能与莫里斯一次性和eFAST方法进行比较.
主要成果:
- 对于生物ABM,SMoRe GloS准确地恢复了全球敏感度指数.
- 与复杂ABM的eFAST相比,实现了大量的计算加快 (分钟与天)
- 成功估计了替代模型中未明确显示的参数的灵敏度.
结论:
- 在复杂的ABM中,SMoRe GloS为GSA提供了一个计算可行的解决方案.
- 增强不确定性量化和从计算上昂贵的模型预测的信心.
- 促进对生物系统中的模型行为和参数影响进行更深入的探索.
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