About this Research Topic
The topic Biophysical Mechanisms of Cardiac Arrhythmias is a part of the Networks in the Cardiovascular System section of Frontiers in Network Physiology.
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. The topic explores the effects of cardiac arrythmias on cardiovascular and physiological networks, and the utility of network approaches to model and analyze the intricate connections within cardiovascular systems.
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.
The research topic not only aims to unravel the biophysical mechanisms of cardiac arrhythmias but also seeks to translate these findings into clinical applications. Understanding the role of network dynamics in arrhythmogenesis could inform the development of targeted therapies and interventions.
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. The topic explores the effects of cardiac arrythmias on cardiovascular and physiological networks, and the utility of network approaches to model and analyze the intricate connections within cardiovascular systems.
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.
The research topic not only aims to unravel the biophysical mechanisms of cardiac arrhythmias but also seeks to translate these findings into clinical applications. Understanding the role of network dynamics in arrhythmogenesis could inform the development of targeted therapies and interventions.
Keywords: Network Physiology, Cardiac Arrhythmias, biophysical mechanisms
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