Cardiac arrhythmias present a significant challenge in cardiovascular medicine, affecting millions of individuals worldwide. Understanding the biophysical mechanisms underlying these arrhythmias is crucial for advancing diagnostic and therapeutic strategies. The research topic delves into a multidisciplinary approach that combines insights from cardiovascular medicine and bioinformatics to explore the intricate web of physiological networks governing cardiac function. Cardiac arrhythmias can arise from diverse causes, including genetic predispositions, structural heart diseases, or metabolic imbalances. Investigating these complex mechanisms requires a comprehensive understanding of the cardiovascular networks orchestrating normal cardiac rhythm. By employing advanced computational techniques, the dynamic interactions among various components, such as ion channels, signaling pathways, and cellular electrophysiology, could be captured to elucidate the emergent properties contributing to arrhythmogenesis. Incorporating artificial intelligence (AI) into the research framework enhances the ability to analyze large-scale, heterogeneous complex datasets, and the utility of machine learning, trained on diverse sources of biological information, could enable the identification of novel patterns and predictive models for cardiac arrhythmias.

This Research Topic aims to unravel the biophysical mechanisms of cardiac arrhythmias and translate these findings into clinical applications. Understanding the role of network dynamics in arrhythmogenesis could inform the development of targeted therapies and interventions. Specific questions to be addressed include: What are the key biophysical interactions that lead to arrhythmias? How can computational models and AI be leveraged to predict arrhythmogenic events? What novel therapeutic targets can be identified through a network physiology approach?

To gather further insights into the biophysical mechanisms of cardiac arrhythmias, we welcome articles addressing, but not limited to, the following themes:
- The role of ion channels and cellular electrophysiology in arrhythmogenesis
- Genetic and molecular determinants of cardiac arrhythmias
- Computational modeling of cardiovascular networks
- Application of AI and machine learning in arrhythmia prediction and diagnosis
- Network-based approaches to therapeutic intervention
- Impact of structural heart diseases on cardiac rhythm
- Metabolic influences on cardiac electrophysiology
- Translational research from bench to bedside in arrhythmia management

Submit your idea

Impact

Frontiers in Network Physiology

Submit your idea

Impact