ArticleBiophysical journal2013
Parameter sensitivity analysis of stochastic models provides insights into cardiac calcium sparks.
Article in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Mechanistic modeling of cell viability assays with in silico lineage tracing.PLoS computational biology · 2025Article
- Computational Modeling of Cardiac Electrophysiology.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Dual effects of the small-conductance CaAmerican journal of physiology. Heart and circulatory physiology · 2023Article
- Coupling and heterogeneity modulate pacemaking capability in healthy and diseased two-dimensional sinoatrial node tissue models.PLoS computational biology · 2022Article
- Estimating ectopic beat probability with simplified statistical models that account for experimental uncertainty.PLoS computational biology · 2021Article
- Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions.CPT: pharmacometrics & systems pharmacology · 2021Article
- Sex Differences in Drug-Induced Arrhythmogenesis.Frontiers in physiology · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning.Biophysical journal · 2020Article
- In silico study of the effects of anti-arrhythmic drug treatment on sinoatrial node function for patients with atrial fibrillation.Scientific reports · 2020Article
- Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.Frontiers in pharmacology · 2019Article
- A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research.Frontiers in physiology · 2018Review
- Editorial: Safety Pharmacology - Risk Assessment QT Interval Prolongation and Beyond.Frontiers in physiology · 2018Article
- An integrative appraisal of mechano-electric feedback mechanisms in the heart.Progress in biophysics and molecular biology · 2017Article
- Ambiguous interactions between diastolic and SR CaThe Journal of general physiology · 2017Review
- Revealing kinetics and state-dependent binding properties of IChaos (Woodbury, N.Y.) · 2017Article
- Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility.The Journal of physiology · 2017Article
- Article
- Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk.MethodsX · 2017Article
- I love it when a plan comes together: Insight gained through convergence of competing mathematical models.Journal of molecular and cellular cardiology · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
We present a parameter sensitivity analysis method that is appropriate for stochastic models, and we demonstrate how this analysis generates experimentally testable predictions about the factors that influence local Ca(2+) release in heart cells. The method involves randomly varying all parameters, running a single simulation with each set of parameters, running simulations with hundreds of model variants, then statistically relating the parameters to the simulation results using regression methods. We tested this method on a stochastic model, containing 18 parameters, of the cardiac Ca(2+) spark. Results show that multivariable linear regression can successfully relate parameters to continuous model outputs such as Ca(2+) spark amplitude and duration, and multivariable logistic regression can provide insight into how parameters affect Ca(2+) spark triggering (a probabilistic process that is all-or-none in a single simulation). Benchmark studies demonstrate that this method is less computationally intensive than standard methods by a factor of 16. Importantly, predictions were tested experimentally by measuring Ca(2+) sparks in mice with knockout of the sarcoplasmic reticulum protein triadin. These mice exhibit multiple changes in Ca(2+) release unit structures, and the regression model both accurately predicts changes in Ca(2+) spark amplitude (30% decrease in model, 29% decrease in experiments) and provides an intuitive and quantitative understanding of how much each alteration contributes to the result. This approach is therefore an effective, efficient, and predictive method for analyzing stochastic mathematical models to gain biological insight.
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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.