神经内分泌-炎症模型在不同实验设计下的参数可辨识性
Parameter identifiability of a neuroendocrine-inflammatory model across experimental designs
- University of South Carolina（南卡罗来纳大学）
- North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过比较三种可辨识性分析方法，发现剖面似然法虽计算昂贵但更准确，且需随实验设计变化而重新计算。
AI中文摘要：
数学建模有助于我们识别和研究复杂的功能机制。这对于理解炎症尤为有用，炎症是一个复杂的、多尺度的过程，与其他生理系统非线性地相互作用。这些模型的全部潜力只有通过与实验数据结合进行参数估计才能实现。然而，要唯一确定模型参数，需要这些参数具有实际可辨识性，这可以通过多种可能不一致的方法来评估。可辨识性也可能随实验设计和观测算子而变化。本研究通过评估神经内分泌-炎症系统耦合模型中的参数可辨识性来探讨这一问题。我们比较了在全局敏感性分析之后的三种工作流程：基于Fisher信息矩阵的可辨识性、带有频率学置信区间的全局优化以及剖面似然法。我们在六个观测算子下比较结果，这些算子有无测量噪声，且根据先前文献均具有实验可行性。我们的结果表明，基于Fisher信息的方法通常导致更大的可辨识参数集，而剖面似然分析导致更少的可辨识参数。这表明，尽管计算成本高昂，剖面似然对于准确评估可辨识性是必要的，并且在实验设计改变时必须重新计算。
英文摘要：
Mathematical modeling helps us identify and investigate complex mechanisms of function. This is especially useful for understanding inflammation, which is a complex, multiscale process that interacts nonlinearly with other physiological systems. The full potential of these models can only be realized when combined with experimental data through parameter estimation. However, uniquely determining model parameters requires that they are practically identifiable, which can be assessed by multiple, possibly inconsistent, methods. Identifiability can also vary with experimental designs and observation operators. This study investigates this issue by assessing parameter identifiability in a coupled model of the neuroendocrine-inflammatory system. We compare three workflows following a global sensitivity analysis: Fisher-information matrix based identifiability, global optimization with frequentist confidence intervals, and the profile-likelihood. We compare results across six observation operators with and without measurement noise, all of which are experimentally feasible given prior literature. Our results show that Fisher-information-based methods typically lead to larger sets of identifiable parameters while profile-likelihood analyses lead to a smaller number of identifiable parameters. This suggests that, while computationally expensive, the profile-likelihood is necessary for accurately assessing identifiability, and must be recalculated when the experimental design is altered.
