COMPUTATIONAL TOOLS FOR MULTISCALE HEART MODELING

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 这个项目是与杜克大学的Henriquez博士的计算电生理学小组合作的。该项目旨在使用心脏组织模型，这些模型足够有效地模拟大块组织，甚至整个心脏的行为，但仍然保留与细胞和膜行为及其模拟模型的联系。 这种类型的研究最常用的模型采用连续bidomain模型，这种方法将细胞内和细胞外空间平均化，以形成由膜分隔的连续交错体积。然而，这种类型的模型没有考虑实际膜的形状和位置，也没有处理细胞是离散实体的事实。 为了进一步分析bidomain方法固有的平均效应，更具体地说。为了将平均特性与潜在的组织病理学联系起来，该项目旨在使用细胞尺度的离散几何和计算模型来模拟心脏组织中去极化前沿的传播。 应用焦点已经从基于少量（30-100）离散细胞的模型研究心脏组织的被动电特性发展到现在包括激励的传播，仍然同样强调在各种条件下确定激活的速度和有效性的参数。 主要的医学目标仍然是开发一种方法，该方法允许将来自正常和病理组织的组织学的细胞水平特征直接并入到模拟框架中，该模拟框架可以生成驱动比多曼制剂的相关中尺度参数。 通过这种方式，我们可以考虑离子浓度、细胞形状、间隙连接电导率、细胞外体积等的许多病理生理变化，这些因素与动作电位传播中的良性波动有关，也与危及生命的心律失常有关。 我们的建模方法是唯一适合在这一领域的参数研究，仅仅是因为它的物理规模小，控制相关参数的水平。我们能够在通常用于单细胞模型的规模下研究组织制备，从而可以观察细胞和细胞膜以及细胞基质和间隙空间对激发扩散的贡献。在这种情况下（可能还有许多其他情况下），一个孤立的细胞的反应与同一个细胞作为合胞体（如心肌）的一部分时的反应大不相同。 我们已经实现的方法是基于创建几个细胞的横截面从图像和参数从心脏细胞和组织结构的显微镜研究，然后扩展或挤压这些横截面，以生成高度逼真的三维模型的组织。 通过管理边界形状，可以生成与其他类似块互锁的细胞构建块，并且这可以生长以形成所需的那么大的组织块，并且仍然是计算易处理的。 从这些块的网络中，我们创建了非常高分辨率的网格，使用数百万个有限元来表示连接到组织中的多达100个细胞。 然后，有限元方法提供了计算被动电特性的数值框架，也扩展去极化波前的电活动。 我们将这种方法称为“微域”，因为它明确地包括表征精细尺度组织行为所必需的微观域。

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.