ArticleScience advances2018
Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.
Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
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- A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes.Royal Society open science · 2025Article
- Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.CPT: pharmacometrics & systems pharmacology · 2025Article
- [Intrinsic steady-state pattern of mouse cardiac electrophysiology: analysis using a characterized quantitative electrocardiogram strategy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling.Bulletin of mathematical biology · 2024Article
- Computational Modeling of Cardiac Electrophysiology.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modelling.Royal Society open science · 2023Article
- Using machine learning surrogate modeling for faster QSP VP cohort generation.CPT: pharmacometrics & systems pharmacology · 2023Article
- The promise of digital healthcare technologies.Frontiers in public health · 2023Review
- Colchicine attenuates the electrical remodeling of post-operative atrial fibrillation through inhibited expression of immune-related hub genes and stabilization of microtubules.International journal of biological sciences · 2023Article
- Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomyocytes.Cardiovascular research · 2022Article
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- Cellular heterogeneity and repolarisation across the atria: an in silico study.Medical & biological engineering & computing · 2022Article
- Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.Science advances · 2022Article
- Biological noise is a key determinant of the reproducibility and adaptability of cardiac pacemaking and EC coupling.The Journal of general physiology · 2022Review
- Mechanisms of phosphatidylserine influence on viral production: A computational model of Ebola virus matrix protein assembly.The Journal of biological chemistry · 2022Article
- Graph-based homogenisation for modelling cardiac fibrosis.Journal of computational physics · 2022Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
- Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action.Journal of pharmacokinetics and pharmacodynamics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a single mathematical model but with different parameter values, in response to experimental data. To date, this calibration has been largely limited to selecting models that produce outputs that fall within the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set. We demonstrate our technique using a data set from a cardiac electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Not only does our approach accurately capture the variability inherent in the experimental population, but we also demonstrate how the POMs that it produces may be used to extract additional information from the data used for calibration, including improved identification of the differences underlying stratified data. We also show how our approach allows different hypotheses regarding the variability in complex systems to be quantitatively compared.
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