Grid Based Modeling of Electrical Propagation in Excitable Tissue

可兴奋组织中电传播的基于网格的建模

• 批准号: 7532364
• 负责人: Jack W Buchanan
• 金额: $ 20.64万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7532364
• 关键词: Adopted; Anatomy; Architecture; Arrhythmia; Biological; Biomedical Engineering; Brain; Cellular Membrane; Characteristics; Code; Collaborations; Communication; Computer Simulation; Computers; Coupling; Development; Dimensions; Disease; Elements; Environment; Epilepsy; Extensible Markup Language; Extracellular Space; Fiber; Gastrointestinal tract structure; Health Sciences; Heart; Human; Individual; International; Ischemia; Knowledge; Language; Lateral; Libraries; Link; Mathematics; Measures; Membrane; Metabolism; Methods; Modeling; Muscle; Nature; Nerve; Neurosciences; Performance; Physiological; Physiology; Property; Public Health; Rest; Running; Science; Speed; Techniques; Technology; Time; Tissues; Universities; base; cluster computing; computer code; distributed memory; extracellular; interest; membrane model; motility disorder; multi-scale modeling; multicore processor; open source; parallel computer; parallel computing; performance site; shared memory; simulation; size; software development; success; tool

DESCRIPTION (provided by applicant): We propose to develop, construct, and refine mathematical and computational multi-scale models which will link macroscopic electrical impulse propagation in the heart to underlying membrane-based sub-cellular ionic currents and other intercellular and intracellular metabolic processes, in ways which preserve anatomical architecture of the heart. Such a model will incorporate more realistic physiology and anatomy while avoiding many of the spatial averaging problems of current bi-domain models. Individual sub-cellular and membrane parameters cannot be measured during propagation. By creating propagating models which include these parameters, we seek to create a new understanding of electrical impulse propagation in the heart and in other excitable tissue, such as nerve and muscle. Moreover similar techniques can also be used to model abnormal electrical activity in the brain, which contributes to epilepsy, and abnormal electrical activity in the gastrointestinal tract, a major cause of GI motility disorders. Most previous large scale models of this type have incorporated various simplifications of the cellular architecture in the interest of computational efficiency. Such assumptions have produced results which have not always withstood close experimental scrutiny. In order to allow for fundamentally important tissue architectural complexities, we will continue the development of new modeling techniques which bridge across scales and which employ high order explicit time-integrators so that they can run efficiently using parallel computation on distributed memory clusters of multiprocessors. This will allow for efficient simulations of an entire ventricle or whole heart without averaging out the effects of the discrete cellular nature of the heart. Since both the sub-cellular and macro aspects of these studies can be treated with varying degrees of complexity, we will incorporate modular libraries, starting with the library developed using cellML, an XML derived modeling language, under the IUPS Physiome project. These modular libraries can be used to make trade-offs between complexity and speed of execution which are appropriate for a given model. We have already had substantial success at employing newer explicit numerical integration techniques in both physical and biological problems. In order to further exploit these newer techniques in a biological environment in a way which is efficient and scalable, new university collaborations have been formed between the Biomedical Engineering Department, located at the Health-Science Center in Memphis, and the Mathematics Department, located on the main campus in Knoxville. We will create a modest sized cluster of parallel computers at each of the performance sites with the ability to link them across the network with very high performance communications channels. Software developed in this more coarsely linked, clustered environment will be useful for constructing even larger scale models to run in computational grid environments of hundreds to thousands of processors and associated distributed memory. PUBLIC HEALTH RELEVANCE After 50 years of experimentation and modeling, it is still not known whether lethal cardiac arrhythmias usually arise from a single "irritable" focus (enhanced automaticity) and are then propagated to the rest of the heart or, alternatively, that the primary abnormality is a disorder of propagation itself (reentry). Similarly, in the neurosciences, it is not really known how often epilepsy is due to a single "irritable" focus in the brain and how often the primary disorder is in the propagation of impulses through connecting fibers of the brain itself. We seek support to develop modeling tools based on newer mathematical techniques and newer knowledge of anatomy and physiology to try to answer these questions.

描述(申请人提供)：我们建议开发、构建和改进数学和计算的多尺度模型，将心脏中宏观的电脉冲传播与基于膜的亚细胞离子电流和其他细胞间和细胞内代谢过程联系起来，以保持心脏的解剖结构。这样的模型将结合更真实的生理学和解剖学，同时避免当前双域模型的许多空间平均问题。在传播过程中不能测量单个亚细胞和膜参数。通过创建包含这些参数的传播模型，我们寻求对心脏和其他可兴奋组织(如神经和肌肉)中的电脉冲传播有一个新的理解。此外，类似的技术也可以用来模拟大脑中异常的电活动，这是导致癫痫的原因，以及胃肠道的异常电活动，这是胃肠动力障碍的主要原因。为了提高计算效率，大多数以前的这种类型的大规模模型都包含了对细胞结构的各种简化。这样的假设产生的结果并不总是经得起严密的实验检验。为了考虑到根本上重要的组织结构复杂性，我们将继续开发新的建模技术，这些技术跨越不同的尺度，并采用高阶显式时间积分器，以便它们可以在多处理器的分布式内存集群上使用并行计算高效运行。这将允许有效地模拟整个脑室或整个心脏，而不会平均排除心脏离散细胞性质的影响。由于这些研究的亚细胞和宏观方面都可以用不同程度的复杂程度来处理，我们将合并模块化的库，首先是在iUPS Physiome项目下使用cell ML(一种XML派生的建模语言)开发的库。这些模块化的库可以用来在复杂性和执行速度之间进行权衡，这适合于给定的模型。我们已经在物理和生物问题中使用较新的显式数值积分技术方面取得了实质性的成功。为了在生物环境中以高效和可扩展的方式进一步利用这些新技术，位于孟菲斯健康科学中心的生物医学工程系和位于诺克斯维尔主校园的数学系之间形成了新的大学合作关系。我们将在每个性能站点创建一个中等规模的并行计算机集群，能够通过非常高性能的通信通道通过网络将它们链接起来。在这种更粗链接的集群环境中开发的软件将有助于构建更大规模的模型，以便在由数百到数千个处理器和相关的分布式内存组成的计算网格环境中运行。 公共卫生相关性经过50年的实验和建模，目前尚不清楚致命性心律失常通常是由单个“易激”灶(增强的自律性)引起，然后传播到心脏的其他部位，还是主要异常是传播本身的障碍(折返性)。同样，在神经科学中，人们并不真正知道癫痫是由大脑中一个单一的“易怒”灶引起的，也不知道通过大脑本身的连接纤维传播冲动的主要障碍有多频繁。我们寻求支持，基于更新的数学技术和更新的解剖学和生理学知识来开发建模工具，试图回答这些问题。