Karl Franzens University Graz

Graz University of Technology 


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Physics of the heart: understanding the dynamics of cardiac electrophysiology
Prof. Jordi Heijman
Medizinische Universität Graz
16:15 - 17:15 Tuesday 13 January 2026 TUG HS P2

The heart is a fascinating organ: the interplay of a limited number of fundamental physical concepts produces a highly complex and adaptive system that works relentlessly during our lifetime. Despite its robustness, disturbances in the heart’s rhythm, i.e., cardiac arrhythmias, remain a very common cause of death and disability. Experimental advances, including advanced microscopy, imaging and electrophysiological recordings have enabled an increasingly detailed characterization of the mechanisms underlying cardiac arrhythmias. However, integrating this information and taking into account the complex, non-linear interactions between different regulators has proven challenging. Computational modeling of cardiac electrophysiology is an emerging approach to better understand arrhythmogenic risk and develop personalized therapies. In this presentation, we discuss advances in computational modeling at the cellular [1], organ [2] and patient levels [3], with a particular focus on the dynamics of cardiac electrophysiological activity, touching upon areas such as drug development and safety, the generation of digital twins, and virtual clinical trials.

Relevant references:
1. Meier S, Dobrev D, Volders PGA, Heijman J. Computational modelling of the pro- and antiarrhythmic effects of atrial high rate-dependent trafficking of small-conductance calcium-activated potassium channels. J Physiol. 2025 Jul 20:10.1113/JP288659. doi: 10.1113/JP288659.
2. Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev. 2024 Jul 1;104(3):1265-1333. doi: 10.1152/physrev.00017.2023.
3. Cai M, Barrios-Espinosa C, Rienstra M, Crijns HJGM, Schotten U, Heijman J. Optimizing atrial fibrillation management using a novel patient-level computational model. Med. 2025 Oct 31:100896. doi: 10.1016/j.medj.2025.100896.