病毒动态的宿主内部模型的结构和实际可识别性 - - 综述
Necibe Tuncer1, Maia Martcheva2, Stanca M Ciupe3
1Department of Mathematics, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USA.
Current opinion in systems biology
|November 28, 2025
概括
在病毒动态模型中估计参数是具有挑战性的,因为参数相关性. 这项研究审查了宿主病毒模型中的识别方法,并建议改进更好的实验设计.
科学领域:
- 数学生物学 数学生物学
- 病毒学 病毒学
- 系统生物学 系统生物学
背景情况:
- 主体内病毒动态模型使用普通微分方程 (ODEs) 来描述病毒的行为.
- 从噪音数据进行参数估计受阻于ODE模型内的内在参数相关性.
- 这些相关性可以导致在模型拟合中产生非唯一的解决方案,使分析复杂化.
研究的目的:
- 审查评估主体内基本病毒动态模型中的结构性和实际可识别性的方法.
- 为提高这些模型的可识别性提供指导方针.
- 探索应用识别技术在更复杂的ODE模型中的挑战和进步.
主要方法:
- 对宿主内病毒ODE模型的结构和实际识别现有文献的审查.
- 分析参数相关性及其对模型适配的影响.
- 讨论延迟,部分和随机微分方程模型的扩展.
主要成果:
- 可识别性分析对于理解病毒动态模型中的参数估计挑战至关重要.
- 参数相关性是不可识别的主要来源.
- 建议制定指导方针,以提高模型的识别能力.
结论:
- 结构性和实际的可识别性是主体内病毒动态中可靠的参数估计的关键.
- 解决参数相关性可以提高模型可靠性.
- 可识别性分析应指导最佳实验设计,以便更准确的病毒动态建模.
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