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Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation

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Abstract 

Customization of cardiac action potential models has become increasingly important with the recognition of patient-specific models and virtual patient cohorts as valuable predictive tools. Nevertheless, developing customized models by fitting parameters to data poses technical and methodological challenges: despite noise and variability associated with real-world datasets, traditional optimization methods produce a single “best-fit” set of parameter values. Bayesian estimation methods seek distributions of parameter values given the data by obtaining samples from the target distribution, but in practice widely known Bayesian algorithms like Markov chain Monte Carlo tend to be computationally inefficient and scale poorly with the dimensionality of parameter space. In this paper, we consider two computationally efficient Bayesian approaches: the Hamiltonian Monte Carlo (HMC) algorithm and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. We find that both methods successfully identify distributions of model parameters for two cardiac action potential models using model-derived synthetic data and an experimental dataset from a zebrafish heart. Although both methods appear to converge to the same distribution family and are computationally efficient, HMC generally finds narrower marginal distributions, while ABC-SMC is less sensitive to the algorithmic settings including the prior distribution.

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The data used for this study are available upon request.

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The code used for this study is available upon request.

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Funding

This study was supported by NSF grants CNS-2028677 and CMMI-1762553 and by NIH grant 1R01HL143450.

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Author notes

  1. Alejandro Nieto Ramos

    Present address: Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA

Authors and Affiliations

  1. School of Mathematical Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, 14623, Rochester, NY, USA

    Alejandro Nieto Ramos

  2. School of Physics, Georgia Institute of Technology, 837 State Street NW, 30332, Atlanta, GA, USA

    Flavio H. Fenton

  3. School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street, 30308, Atlanta, GA, USA

    Elizabeth M. Cherry

Authors
  1. Alejandro Nieto Ramos
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  2. Flavio H. Fenton
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  3. Elizabeth M. Cherry
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Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by Alejandro Nieto Ramos. The first draft of the manuscript was written by Alejandro Nieto Ramos and all authors contributed to and commented on subsequent versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Elizabeth M. Cherry.

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The authors declare no competing interests.

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Supplementary file 1 (pdf 1961 KB)

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Nieto Ramos, A., Fenton, F.H. & Cherry, E.M. Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation. Med Biol Eng Comput 61, 75–95 (2023). https://doi.org/10.1007/s11517-022-02685-y

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  • DOI: https://doi.org/10.1007/s11517-022-02685-y

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