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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.


Variance based main effect indices for each emulator.Main effect index of each emulator (rows) to each input (columns), describing the proportion of the output variance that can be accounted for by variance on the input.
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pone.0130252.g008: Variance based main effect indices for each emulator.Main effect index of each emulator (rows) to each input (columns), describing the proportion of the output variance that can be accounted for by variance on the input.

Mentions: Fig 8 shows the main effect indices calculated for each input, and for each emulator. These indices mirror the information shown in the mean effects plots, and indicate, for example, that GK1 and Gb are the inputs that had the most influence on resting voltage. The sum of the main effect indices for was close to 1 (>98%) for all outputs except the APDr (85%) and the minimum DI (26%). These lower values indicate that 15% of the APDr emulator variance and 74% of the minimum DI emulator variance could be accounted for by interaction effects.


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

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

Variance based main effect indices for each emulator.Main effect index of each emulator (rows) to each input (columns), describing the proportion of the output variance that can be accounted for by variance on the input.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130252.g008: Variance based main effect indices for each emulator.Main effect index of each emulator (rows) to each input (columns), describing the proportion of the output variance that can be accounted for by variance on the input.
Mentions: Fig 8 shows the main effect indices calculated for each input, and for each emulator. These indices mirror the information shown in the mean effects plots, and indicate, for example, that GK1 and Gb are the inputs that had the most influence on resting voltage. The sum of the main effect indices for was close to 1 (>98%) for all outputs except the APDr (85%) and the minimum DI (26%). These lower values indicate that 15% of the APDr emulator variance and 74% of the minimum DI emulator variance could be accounted for by interaction effects.

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.