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.
Estimated correlations between parameters and variables
correlations.tar.bz2
The elementary effects (mean and sdev) of each parameter on each variable
elementary_effects.tar.bz2
The elementary effects (mean and sdev) of each parameter on each variable (RData)
virtppl.ee.RData
Estimated correlations between elementary effects and parameters (t = 1 minute)
effect.corr.1m.RData
Estimated correlations between elementary effects and parameters (t = 1 hour)
effect.corr.1h.RData
Estimated correlations between elementary effects and parameters (t = 1 day)
effect.corr.1d.RData
Estimated correlations between elementary effects and parameters (t = 1 week)
effect.corr.1w.RData
Estimated correlations between elementary effects and parameters (t = 4 weeks)
effect.corr.4w.RData
Individual simulations, 1 minute after perturbation
ions, 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.RData
Individual simulations, 1 hour after perturbation
This 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.RData
Individual simulations, 1 day after perturbation
This 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.RData
Individual simulations, 1 week after perturbation
This 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.RData
Individual simulations, 4 weeks after perturbation
This 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