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Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.

Chang ET, Strong M, Clayton RH - PLoS ONE (2015)

Bottom Line: In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators.We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents.We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models.

View Article: PubMed Central - PubMed

Affiliation: Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, United Kingdom; Department of Computer Science University of Sheffield, Sheffield, United Kingdom.

ABSTRACT
Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model runs. In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators. Using this approach we examined how eight outputs describing the action potential shape and action potential duration restitution depend on six inputs, which we selected to be the maximum conductances in the Luo-Rudy 1991 model. We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents. We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models.

No MeSH data available.


Sensitivity indices calculated with PLS technique.Sensitivity index of each emulator (rows) to each input (columns).
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pone.0130252.g009: Sensitivity indices calculated with PLS technique.Sensitivity index of each emulator (rows) to each input (columns).

Mentions: To provide a comparison with the main effects indices obtained from the GP emulators and shown in Fig 8, we also calculated regression coefficients for the LR1991 model using partial least squares (PLS) regression [19]. These coefficients were obtained from the design and test data used to construct the GP emulators, and are shown in Fig 9. These regression coefficients indicate how each output change with each input. Positive values indicate that the output increases as the input increases, whereas negative values indicate that the output decreases as the input increases. The PLS regression coefficients and main effects indices obtained from the GP emulators provide different ways of assessing the way that inputs affect outputs. However the magnitude of the PLS regression coefficients showed good agreement with the main effects indices from the GP emulators. Similar PLS regression coefficients could be obtained using alternative design data drawn from multivariate normal distributions rather than the uniform distribution of the combined design and test data used to construct the GP emulators.


Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.

Chang ET, Strong M, Clayton RH - PLoS ONE (2015)

Sensitivity indices calculated with PLS technique.Sensitivity index of each emulator (rows) to each input (columns).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482712&req=5

pone.0130252.g009: Sensitivity indices calculated with PLS technique.Sensitivity index of each emulator (rows) to each input (columns).
Mentions: To provide a comparison with the main effects indices obtained from the GP emulators and shown in Fig 8, we also calculated regression coefficients for the LR1991 model using partial least squares (PLS) regression [19]. These coefficients were obtained from the design and test data used to construct the GP emulators, and are shown in Fig 9. These regression coefficients indicate how each output change with each input. Positive values indicate that the output increases as the input increases, whereas negative values indicate that the output decreases as the input increases. The PLS regression coefficients and main effects indices obtained from the GP emulators provide different ways of assessing the way that inputs affect outputs. However the magnitude of the PLS regression coefficients showed good agreement with the main effects indices from the GP emulators. Similar PLS regression coefficients could be obtained using alternative design data drawn from multivariate normal distributions rather than the uniform distribution of the combined design and test data used to construct the GP emulators.

Bottom Line: In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators.We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents.We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models.

View Article: PubMed Central - PubMed

Affiliation: Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, United Kingdom; Department of Computer Science University of Sheffield, Sheffield, United Kingdom.

ABSTRACT
Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model runs. In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators. Using this approach we examined how eight outputs describing the action potential shape and action potential duration restitution depend on six inputs, which we selected to be the maximum conductances in the Luo-Rudy 1991 model. We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents. We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models.

No MeSH data available.