Boundary effects influence velocity of transverse propagation of simulated cardiac action potentials.
BACKGROUND
We previously demonstrated that transverse propagation of excitation (cardiac action potentials simulated with PSpice) could occur in the absence of low-resistance connections (gap--junction channels) between parallel chains of myocardial cells. The transverse transmission of excitation between the chains was strongly dependent on the longitudinal resistance of the interstitial fluid space between the chains: the higher this resistance, the closer the packing of the parallel chains within the bundle. The earlier experiments were carried out with 2-dimensional sheets of cells: 2 x 3, 3 x 4, and 5 x 5 models (where the first number is the number of parallel chains and the second is the number of cells in each chain). The purpose of the present study was to enlarge the model size to 7 x 7, thus enabling the transverse velocities to be compared in models of different sizes (where all circuit parameters are identical in all models). This procedure should enable the significance of the role of edge (boundary) effects in transverse propagation to be determined.


RESULTS
It was found that transverse velocity increased with increase in model size. This held true whether stimulation was applied to the entire first chain of cells or only to the first cell of the first chain. It also held true for retrograde propagation (stimulation of the last chain). The transverse resistance at the two ends of the bundle had almost no effect on transverse velocity until it was increased to very high values (e.g., 100 or 1,000 megohms).


CONCLUSIONS
Because the larger the model size, the smaller the relative edge area, we conclude that the edge effects slow the transverse velocity.
Sperelakis
Nicholas
N
Dept. of Molecular & Cellular Physiology, University of Cincinnati College of Medicine, Cincinnati, OH 45267-0576, USA. spereln@ucmail.uc.edu
Kalloor
Bijoy
B
Ramasamy
Lakshminarayanan
L
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D017629:Gap Junctions / Q000378:metabolism; D006321:Heart / Q000502:physiology*; D006329:Heart Conduction System / Q000502:physiology; D006801:Humans; D015640:Ion Channel Gating / Q000502:physiology; D008954:Models, Biological; D008962:Models, Theoretical; D009206:Myocardium / Q000166:cytology / Q000473:pathology; D032383:Myocytes, Cardiac / Q000502:physiology