DOI: 10.5061/dryad.h3s0r
title: Data from: Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: towards individualized models of whole-body physiology

description: 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.
description: Estimated correlations
 between parameters and variablescorrelations.tar.bz2The elementary effects (mean and sdev) of each parameter on each variableelementary_effects.tar.bz2The elementary effects (mean and sdev) of each parameter on each variable (RData)virtppl.ee.RDataEstimated correlations between elementary effects and parameters (t = 1 minute)effect.corr.1m.RDataEstimated correlations between elementary effects and parameters (t = 1 hour)effect.corr.1h.RDataEstimated correlations between elementary effects and parameters (t = 1 day)effect.corr.1d.RDataEstimated correlations between elementary effects and parameters (t = 1 week)effect.corr.1w.RDataEstimated correlations between elementary effects and parameters (t = 4 weeks)effect.corr.4w.RDataIndividual simulations, 1 minute after perturbationions, 1 minute after the perturbation. The columns encompass all parameters (prefixed with "p_") and all variables (prefixed with "v_"). The odd-numbered rows record the state of the model prior to the perturbation, and the even-numbered rows record the state of the model after the perturbation.virtppl.1m.RDataIndividual simulations, 1 hour after perturbationThis RData file contains a table of all the simulations, 1 hour after the perturbation. The columns encompass all parameters (prefixed with "p_") and all variables (prefixed with "v_"). The odd-numbered rows record the state of the model prior to the perturbation, and the even-numbered rows record the state of the model after the perturbation.virtppl.1h.RDataIndividual simulations, 1 day after perturbationThis RData file contains a table of all the simulations, 1 day after the perturbation. The columns encompass all parameters (prefixed with "p_") and all variables (prefixed with "v_"). The odd-numbered rows record the state of the model prior to the perturbation, and the even-numbered rows record the state of the model after the perturbation.virtppl.1d.RDataIndividual simulations, 1 week after perturbationThis RData file contains a table of all the simulations, 1 week after the perturbation. The columns encompass all parameters (prefixed with "p_") and all variables (prefixed with "v_"). The odd-numbered rows record the state of the model prior to the perturbation, and the even-numbered rows record the state of the model after the perturbation.virtppl.1w.RDataIndividual simulations, 4 weeks after perturbationThis RData file contains a table of all the simulations, 4 weeks after the perturbation. The columns encompass all parameters (prefixed with "p_") and all variables (prefixed with "v_"). The odd-numbered rows record the state of the model prior to the perturbation, and the even-numbered rows record the state of the model after the perturbation.virtppl.4w.RData