Population-Based Mathematical Modeling Facilitates the Interpretation of Dynamic Clamp Experiments in Cardiomyocytes

Devenyi, Ryan A.; Ortega, Francis A.; Krogh‐Madsen, Trine; Christini, David J.; Sobie, Eric A.

doi:10.1016/j.bpj.2015.11.3125

Cited by 1 publication

(2 citation statements)

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“…Care must be taken, however, to ensure that the ensemble size does not dramatically increase and slow down the computation. There may be parallels with population-based approaches currently in use by the broader modelling community [43][44][45][46][47][48], especially if the ensemble members use different parameter values.…”

Section: (A) Connections To Uncertainty Quantificationmentioning

confidence: 99%

“…A number of previous efforts have focused on improving the accuracy and reliability of cardiac models in various ways. Such efforts include making improvements to the models themselves, such as through better methods for parameter estimation [34][35][36][37][38] or through the development and use of models with fewer parameters [19,[39][40][41] to reduce issues of identifiability [42], by seeking to improve predictions through the use of a population of models [43][44][45][46][47][48], or by tracking uncertainty in aspects of the model and propagating that uncertainty forward to predictions [49,50]. These approaches may lead to improvements in the accuracy of models and their predictions.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics

Hoffman

Cherry²

2020

Phil. Trans. R. Soc. A.

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Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm’s sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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