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Updated: Jun 10, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Using computational modeling to predict arrhythmogenesis and antiarrhythmic therapy.

Jonathan D Moreno¹, Colleen E Clancy

¹Tri-Institutional MD-PhD Program, Physiology, Biophysics and Systems Biology Graduate Program Weill Cornell Medical College / The Rockefeller University / Sloan-Kettering Cancer Institute Weill Medical College of Cornell University 1300 York Avenue New York, New York, USA, 10021.

Drug Discovery Today. Disease Models

|July 24, 2010

Summary

Computational modeling advances our understanding of arrhythmia mechanisms. Simulations reveal how genetic mutations and drug effects impact heart rhythms, aiding in predicting and managing arrhythmias.

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Published on: May 27, 2022

Area of Science:

Cardiovascular Physiology
Computational Biology
Pharmacology

Background:

Computational modeling is a rapidly advancing field for understanding cardiac electrophysiology.
Cellular action potential models have evolved significantly, incorporating genetic mutations.
Understanding arrhythmia mechanisms requires integrating multi-scale physiological data.

Purpose of the Study:

To review recent advances in computational modeling for arrhythmia prediction.
To explore how genetic mutations and drug interactions influence cardiac arrhythmias.
To discuss the application of modeling in assessing drug safety and efficacy.

Main Methods:

Review of cellular action potential models and their evolution.
Incorporation of genetic mutations into computational models.
Simulation of drug blockade effects on cardiac electrophysiology.
Analysis of multi-scale spatial data from channel to tissue level.

Main Results:

Simulations elucidate situation-specific mechanisms causing varied phenotypes from single genotypes.
Modeling reveals non-intuitive drug effects, including paradoxical exacerbation of arrhythmias.
Quantification of arrhythmia indices across different spatial scales is discussed.
hERG channel modeling aids in assessing drug-induced repolarization changes.

Conclusions:

Computational modeling is crucial for deciphering complex arrhythmia pathophysiology.
Integrating genetic and pharmacological data into models enhances predictive power.
Multi-scale modeling provides insights into drug safety and personalized arrhythmia treatment.