ArticleJournal of molecular and cellular cardiology2016
Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?
Article in Journal of molecular and cellular cardiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 142 citations in OpenAlex.
- Determining properties of human-induced pluripotent stem cell-derived cardiomyocytes using spatially resolved electromechanical metrics.The Journal of physiology · 2026Article
- Models of cardiomyocyte-non-myocyte electrical interactions.The Journal of physiology · 2026Review
- Reproducing Cardiac Ionic Model Properties Using a Discrete-Time Model.Computing in cardiology · 2025Article
- 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
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes.Royal Society open science · 2025Article
- Article
- Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data assimilation.Biophysical journal · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Article
- Neural network emulation of the human ventricular cardiomyocyte action potential: a tool for more efficient computation in pharmacological studies.bioRxiv : the preprint server for biology · 2023Article
- Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.Bulletin of mathematical biology · 2023Article
- A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations.Computing in cardiology · 2023Article
- Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.Archives of toxicology · 2023Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- Calibration of Cohorts of Virtual Patient Heart Models Using Bayesian History Matching.Annals of biomedical engineering · 2023Article
- A pilot study of ion current estimation by ANN from action potential waveforms.Journal of biological physics · 2022Article
- Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.Science advances · 2022Article
- A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data.Nature communications · 2022Article
- A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.Bulletin of mathematical biology · 2022Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
Cardiac electrophysiology models have been developed for over 50years, and now include detailed descriptions of individual ion currents and sub-cellular calcium handling. It is commonly accepted that there are many uncertainties in these systems, with quantities such as ion channel kinetics or expression levels being difficult to measure or variable between samples. Until recently, the original approach of describing model parameters using single values has been retained, and consequently the majority of mathematical models in use today provide point predictions, with no associated uncertainty. In recent years, statistical techniques have been developed and applied in many scientific areas to capture uncertainties in the quantities that determine model behaviour, and to provide a distribution of predictions which accounts for this uncertainty. In this paper we discuss this concept, which is termed uncertainty quantification, and consider how it might be applied to cardiac electrophysiology models. We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.