MATHEMATICAL MODELING AND SIMULATION

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 数学建模与仿真 子项目说明 作为一个中心，我们已经建立了生物电场模拟领域的专业知识， 目前的资助期限，并建议继续使这种形式的模拟成为我们未来研究的核心 活动在中心开始时，我们的重点是躯干和头部的被动电特性及其 响应内源性生物电源（心脏和大脑）;我们解决了这两个正向问题，基于已知的 源，以及逆问题，在其中，我们试图确定和本地化的生物电源的测量 在身体表面上（或外面）。我们今年继续进行这项研究，并建议 在下一个供资周期继续开展这项工作，并与合作者密切合作。 近年来，我们也开始模拟生物电活动本身，从而研究生物电的性质 这些来源是高度动态的，增加对它们行为的了解将有助于提高我们的能力， 预测疾病中它们的功能和功能障碍的后果。我们建议继续这项研究， 重点模拟心肌缺血和除颤对心脏、癫痫和脑深部的影响 刺激大脑。为了将中心内的发现和计算发展转化为 更广泛的生物医学用户社区，我们将继续开发，发布，发布和支持软件， 结合动态生物电源的模型以及工具，以创建有效的解决方案， 相关的正问题和逆问题。 生物电活动的模拟的一个应用是在计算中的兴奋的传播， 心肌组织的显微模型。 这项研究的目标是解决多尺度中长期存在的差距， 在非常进化和良好表征的心脏细胞行为之间的心脏电生理学建模 膜和模拟整个心脏的电活动。 心脏的模拟主要是 因为在从离子通道的随机模型到整个心脏的每个有意义的尺度上都存在模型 和躯干 然而，在每次规模转换时都需要进行简化，因此需要 在一个尺度上的表达被建立为在下一个尺度上的关联表达。 例如，组织模型必须能够 结合细胞行为变化的影响，以模拟或预测病理生理学或 药剂学的机制 还必须直到最近，在这个翻译中， 尺度，微观结构的变化在组织水平模型中找到表达。 我们已经开始解决这个问题 不作为 此外，在此TRD中，我们已经开始探索图形处理等加速硬件的使用 单元，GPU的，以及一般来说，用于生物医学模拟的流架构。由于速度和效率， GPU的增长速度甚至快于传统的中央处理器（CPU）， 大多数现代图形处理器中包含的流架构在以下方面具有固有的优势， 可伸缩性

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. MATHEMATICAL MODELING AND SIMULATION Subproject Description As a Center, we have established expertise in the area of simulation in bioelectric fields, have built on that expertise in the current funding period, and propose to continue to make this form of simulation a centerpiece of our future research activities. At the start of the Center our focus was on passive electrical characteristics of the torso and head and their response to endogenous bioelectric sources (the heart and brain); we solved both forward problems, based on known sources, as well as inverse problems, in which we sought to identify and localize bioelectric sources from measurements on (or outside) the body surface. We have continued this research thrust through the current year and propose to continue it in the next funding cycle, working closely with collaborators. In recent years, we have also begun to simulate bioelectric activity itself and thus to study the nature of bioelectric sources; these sources are highly dynamic and increased knowledge of their behavior will help improve our ability to predict the consequences of their function and dysfunction in disease. We propose to continue this research, with emphasis on simulating the effects of myocardial ischemia and defibrillation on the heart and epilepsy and deep brain stimulation in the brain. In order to translate the discoveries and computational developments within the Center to the broader biomedical user community, we will continue to develop, publish, release, and support software that will incorporate models of dynamic bioelectric sources as well as the tools with which to create efficient solutions to the associated forward and inverse problems. One application of the simulation of bioelectric activity has been in the computation of the spread of excitation in microscopic models of myocardial tissue. The goal of this research was to address a longstanding gap in the multiscale modeling of cardiac electrophysiology between the very evolved and well-characterized behavior of cardiac cell membranes and the simulation of electrical activity in the whole heart. Simulation of the heart has advanced mainly because there exist models at each of the meaningful scales from stochastic models of ion channels to the whole heart and torso. However, there is a need for simplification at each transition of scale, and hence a requirement that results at one scale are established as an associated expression at the next scale. For example, a model of tissue must be able to incorporate the effects of changes in the behavior of the cell in order to mimic or predict pathophysiology or the mechanisms of pharmaceutics. It is also essentialand until recently a significant omissionin this translation across scales, that changes in microscopic structure find expression in tissue level models. We have begun to address this omission. In addition, within this TRD we have begun to explore the use of acceleration hardware such as graphical processing units, GPU's, and, in general, streaming architectures for use in biomedical simulation. As the speed and efficiency of GPU's grows at rates even faster than those of conventional central processing units (CPU's), there is a growing consensus that the streaming architecture embodied in most modern graphics processors has inherent advantages in scalability.