Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

• Published in: PLoS computational biology Vol. 8; no. 6; p. e1002571
• Main Authors: Moss, Robert, Grosse, Thibault, Marchant, Ivanny, Lassau, Nathalie, Gueyffier, François, Thomas, S. Randall
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2012; PLOS; Public Library of Science (PLoS)
• Subjects: Biology; Blood pressure; Blood Pressure - physiology; Cardiac Output - physiology; Computational Biology; Computer Simulation; Grants; Health aspects; Humans; Hypertension - physiopathology; Life Sciences; Mathematical models; Medical research; Models, Cardiovascular; Monte Carlo Method; Patients; Physiological aspects; Physiology; Physiology, Pathological; Population; Precision Medicine; Reproducibility of Results; Scale models; Sensitivity analysis; Studies; Urination - physiology; User-Computer Interface; France; Blood Pressure; Hypertension; User-Computer Interface; Reproducibility of Results; Models, Cardiovascular; Computer Simulation; Humans; Cardiac Output; Computational Biology; Individualized Medicine; Urination; Monte Carlo Method
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002571

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.