Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: towards individualized models of whole-body physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton’s whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the grou...

整合多尺度生理数据的数学模型，可为生理与病理生理功能的解析提供重要洞见，最终亦可助力个体化预测医学的发展。本研究提出一套方法，用于对这类复杂多尺度模型中的多参数交互作用开展系统性分析。人体生理学模型通常以亚瑟·盖顿（Arthur Guyton）的全身循环调节模型为基础，或受其启发构建。尽管该模型具有重要学术价值，但此前尚未有针对其开展系统性、全面性敏感性分析的相关研究。因此，本研究以该模型为案例，对所提出的方法进行验证。对盖顿模型的分析结果揭示了模型中大量参数如何协同影响模型动力学特性，以及如何识别出具有研究意义的参数组合。该数据集还包含一个「虚拟人群（virtual population）」，可基于与真实患者相似的生理状态，从中选取「虚拟个体（virtual individuals）」。此项工作为相关研究奠定了基础（原文末尾截断为"lays the grou..."）。