Cell-to-cell Mathematical Modeling of Arrhythmia Phenomena in the Heart
Lopez, G.; Nicolas, A.; Roman, G.; Godinez, R.; Castro, M. A.
Show abstract
With an aperiodic, self-similar distribution of two-dimensional arrangement of atrial cells, it is possible to simulate such phenomena as Fibrillation, Fluttering, and a sequence of Fibrillation-Fluttering. The topology of a network of cells may facilitate the initiation and development of arrhythmias such as Fluttering and Fibrillation. Using a GPU parallel architecture, two basic cell topologies were considered in this simulation, an aperiodic, fractal distribution of connections among 462 cells, and a chessboard-like geometry of 60x60 and 600x600 cells. With a complex set of initial conditions, it is possible to produce tissue behavior that may be identified with arrhythmias. Finally, we found several sets of initial conditions that show how a mesh of cells may exhibit Fibrillation that evolves into Fluttering.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Buffering and total calcium levels determine the presence of oscillatory regimes in cardiac cells 97%
- A new paradigm considering multicellular adhesion, repulsion and attraction represent diverse cellular tile patterns 97%
- Notch signaling and taxis mechanims regulate early stage angiogenesis: A mathematical and computational model 96%
Similar papers in this journal
- Fluid-structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements 96%
- ATP diffusional gradients are sufficient to maintain bioenergetic homeostasis in synaptic boutons lacking mitochondria 95%
- Mesh-Free High-Resolution Simulation Of Cerebrocortical Oxygensupply With Fast Fourier Preconditioning 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.