Axel Loewe1, Eike Moritz Wülfers2,3, Gunnar Seemann4,5,6. 1. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. 2. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg, Bad Krozingen, Medical Center, Computational Modeling Group, Albert-Ludwigs University of Freiburg, Elsässerstr. 2q, 79110, Freiburg, Germany. 3. Faculty of Medicine, University of Freiburg, Freiburg, Germany. 4. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. gunnar.seemann@universitaets-herzzentrum.de. 5. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg, Bad Krozingen, Medical Center, Computational Modeling Group, Albert-Ludwigs University of Freiburg, Elsässerstr. 2q, 79110, Freiburg, Germany. gunnar.seemann@universitaets-herzzentrum.de. 6. Faculty of Medicine, University of Freiburg, Freiburg, Germany. gunnar.seemann@universitaets-herzzentrum.de.
Abstract
BACKGROUND: Complementary to clinical and experimental studies, computational cardiac modeling serves to obtain a comprehensive understanding of the cardiovascular system in order to analyze dysfunction, evaluate existing, and develop novel treatment strategies. OBJECTIVES: We describe the basics of multiscale computational modeling of cardiac electrophysiology from the molecular ion channel to the whole body scale. By modeling cardiac ischemia, we illustrate how in silico experiments can contribute to our understanding of how the pathophysiological mechanisms translate into changes observed in diagnostic tools such as the electrocardiogram (ECG). MATERIALS AND METHODS: Quantitative in silico modeling spans a wide range of scales from ion channel biophysics to ECG signals. For each of the scales, a set of mathematical equations describes electrophysiology in relation to the other scales. Integration of ischemia-induced changes is performed on the ion channel, single-cell, and tissue level. This approach allows us to study how effects simulated at molecular scales translate to changes in the ECG. RESULTS: Ischemia induces action potential shortening and conduction slowing. Hence, ischemic myocardium has distinct and significant effects on propagation and repolarization of excitation, depending on the intramural extent of the ischemic region. For transmural and subendocardial ischemic regions, ST segment elevation and depression, respectively, were observed, whereas intermediate ischemic regions were found to be electrically silent (NSTEMI). CONCLUSIONS: In silico modeling contributes quantitative and mechanistic insight into fundamental ischemia-related arrhythmogenic mechanisms. In addition, computational modeling can help to translate experimental findings at the (sub-)cellular level to the organ and body context (e. g., ECG), thereby providing a thorough understanding of this routinely used diagnostic tool that may translate into optimized applications.
BACKGROUND: Complementary to clinical and experimental studies, computational cardiac modeling serves to obtain a comprehensive understanding of the cardiovascular system in order to analyze dysfunction, evaluate existing, and develop novel treatment strategies. OBJECTIVES: We describe the basics of multiscale computational modeling of cardiac electrophysiology from the molecular ion channel to the whole body scale. By modeling cardiac ischemia, we illustrate how in silico experiments can contribute to our understanding of how the pathophysiological mechanisms translate into changes observed in diagnostic tools such as the electrocardiogram (ECG). MATERIALS AND METHODS: Quantitative in silico modeling spans a wide range of scales from ion channel biophysics to ECG signals. For each of the scales, a set of mathematical equations describes electrophysiology in relation to the other scales. Integration of ischemia-induced changes is performed on the ion channel, single-cell, and tissue level. This approach allows us to study how effects simulated at molecular scales translate to changes in the ECG. RESULTS:Ischemia induces action potential shortening and conduction slowing. Hence, ischemic myocardium has distinct and significant effects on propagation and repolarization of excitation, depending on the intramural extent of the ischemic region. For transmural and subendocardial ischemic regions, ST segment elevation and depression, respectively, were observed, whereas intermediate ischemic regions were found to be electrically silent (NSTEMI). CONCLUSIONS: In silico modeling contributes quantitative and mechanistic insight into fundamental ischemia-related arrhythmogenic mechanisms. In addition, computational modeling can help to translate experimental findings at the (sub-)cellular level to the organ and body context (e. g., ECG), thereby providing a thorough understanding of this routinely used diagnostic tool that may translate into optimized applications.
Authors: R H Clayton; O Bernus; E M Cherry; H Dierckx; F H Fenton; L Mirabella; A V Panfilov; F B Sachse; G Seemann; H Zhang Journal: Prog Biophys Mol Biol Date: 2010-05-27 Impact factor: 3.667
Authors: Bettina C Schwab; Gunnar Seemann; Richard A Lasher; Natalia S Torres; Eike M Wulfers; Maren Arp; Eric D Carruth; John H B Bridge; Frank B Sachse Journal: IEEE Trans Med Imaging Date: 2013-01-17 Impact factor: 10.048
Authors: Axel Loewe; Walther H W Schulze; Yuan Jiang; Mathias Wilhelms; Armin Luik; Olaf Dössel; Gunnar Seemann Journal: Biomed Res Int Date: 2015-10-26 Impact factor: 3.411