COMPUTATIONAL TOOLS FOR MULTISCALE HEART MODELING

点击翻译按钮获取中文摘要

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This project is a collaboration with Dr. Henriquez' Computational Electrophysiology group at Duke University. This project aims at using models of cardiac tissue that are efficient enough to simulation behavior in large pieces of tissue, even whole hearts, but still retain links to cellular and membrane behavior, and their simulation models. The most commonly used models for this type of study employ continuous bidomain models, an approach which averages out the intra- and extracellular spaces to form continuous interleaved volumes separated by a membrane. This type of model, however, does not take into account the shape and location of the actual membrane nor does it deal with the fact that cells are discrete entities. In order to further analyze the effect of averaging intrinsic to the bidomain approach, and, more specifically. to tie the averaged properties to the underlying tissue pathology, the project aims at using discrete geometric and computational models at a cellular scale to perform simulations of the propagation of the depolarization front in cardiac tissue. The application focus has progressed from studying the passive electrical properties of cardiac tissue based on models of small numbers (30-100) of discrete cells to now include the spread of excitation, still with the same emphasis on the parameters that determine the speed and effectiveness of activation under a variety of conditions. The main medical goals remains to develop an approach that allows for the direct incorporation of cellular level features from histology of both normal and pathological tissues into a simulation framework that can generate the associated meso-scale parameters that drive the bidomain formulation. In this way, we can take into account a host of pathophysiological changes in ionic concentrations, cell shape, gap junction conductivity, extracellular volume, etc., factors that are linked to both benign fluctuations in propagation of the action potential but also to life threatening arrhythmias. Our modeling approach is uniquely suited to parameter studies in this domain simply because of its small physical scale and the level of control over relevant parameters. We are able to study a tissue preparation at the scale typically employed for models of single cells and thus can observe contributions from both the cells and cell membrane as well as the cell matrix and interstitial spaces to the spread of excitation. The response of an isolated cell in this context (and probably many other contexts) will be quite different from the response of that same cell when it is part of a syncytium like the myocardium. The approach we have implemented is based on creating cross sections of a few cells from images and parameters from microscopy studies of cardiac cell and tissue structure and then expanding or extruding these cross sections to generate highly realistic three-dimensional models of the tissue. By managing the boundary shape, it is possible to generate building blocks of cells that interlock with other similar blocks and this can grow to form as large a tissue piece as is required and still computational tractable. From these networks of blocks, we create very high resolution meshes, using millions of finite elements to represent up to 100 cells linked into a tissue. The finite element method then provides the numerical framework to compute passive electrical characteristics and also electrical activity of the spreading depolarization wavefront. We have denoted this approach as "microdomain" as it includes explicitly the microscopic domains that are essential to characterize fine scale tissue behavior.