ArticleRoyal Society open science2015
Hodgkin-Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods.
Article in Royal Society open science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- CardioFit: a WebGL-based tool for fast and efficient parametrization of cardiac action potential models to fit user-provided data.Royal Society open science · 2025Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- Training deep neural density estimators to identify mechanistic models of neural dynamics.eLife · 2020Article
- Reducing complexity and unidentifiability when modelling human atrial cells.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Dynamic clamp constructed phase diagram for the Hodgkin and Huxley model of excitability.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- An automatic adaptive method to combine summary statistics in approximate Bayesian computation.PloS one · 2020Article
- Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.Computers in biology and medicine · 2019Review
- Reproducible model development in the cardiac electrophysiology Web Lab.Progress in biophysics and molecular biology · 2018Article
- Cellular function given parametric variation in the Hodgkin and Huxley model of excitability.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Nerve conduction models in myelinated and unmyelinated nerves based on three-dimensional electrostatic interaction.Neural regeneration research · 2018Article
- Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models.Journal of the Royal Society, Interface · 2017Article
- Hierarchical Bayesian inference for ion channel screening dose-response data.Wellcome open research · 2016Article
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4 authors.
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Abstract
As cardiac cell models become increasingly complex, a correspondingly complex 'genealogy' of inherited parameter values has also emerged. The result has been the loss of a direct link between model parameters and experimental data, limiting both reproducibility and the ability to re-fit to new data. We examine the ability of approximate Bayesian computation (ABC) to infer parameter distributions in the seminal action potential model of Hodgkin and Huxley, for which an immediate and documented connection to experimental results exists. The ability of ABC to produce tight posteriors around the reported values for the gating rates of sodium and potassium ion channels validates the precision of this early work, while the highly variable posteriors around certain voltage dependency parameters suggests that voltage clamp experiments alone are insufficient to constrain the full model. Despite this, Hodgkin and Huxley's estimates are shown to be competitive with those produced by ABC, and the variable behaviour of posterior parametrized models under complex voltage protocols suggests that with additional data the model could be fully constrained. This work will provide the starting point for a full identifiability analysis of commonly used cardiac models, as well as a template for informative, data-driven parametrization of newly proposed models.
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