Data from: 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
groundwork for using the Guyton model for in silico exploration of
pathophysiological states and treatment strategies. The results presented
here illustrate several potential uses for the entire dataset of
sensitivity results and the “virtual individuals” that we have generated,
which are included in the supplementary material. More generally, the
presented methodology is applicable to modern, more complex multi-scale
physiological models.