ADVANCED ALGORITHM DEVELOPMENT FOR CRYO EM CALCULATIONS

用于冷冻电镜计算的高级算法开发

• 批准号: 7551188
• 负责人: ESMOND NG
• 金额: $ 17.34万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7551188
• 关键词: 3-Dimensional; Algorithms; Area; Back; Boxing; Cereals; Collaborations; Communication; Computer software; Cryoelectron Microscopy; Data; Dependency; Development; Ensure; Environment; Goals; Image; Lead; Linux; Localized; Maps; Memory; Numbers; Performance; Publishing; Research; Research Personnel; Resolution; Running; Schedule; Scheme; Solutions; Speed; Spiders; Techniques; Technology; Update; Work; base; combinatorial; density; design; distributed memory; experience; improved; parallel computer; parallel computing; particle; preconditioning; programs; reconstruction; research and development; research study; scientific computing; three dimensional structure

The goal of this project is to fill in the gap between research developments in cryo-electron microscopy (cryo-EM) and recent advances in large-scale scientific computing, and thus to better enable high-resolution single particle reconstructions on a routine and timely basis. To accomplish this goal, we will conduct research in the design and implementation of new numerical algorithms that will lead to significant reduction in the amount of work required to perform high-resolution single particle reconstructions. Our work will include improving the efficiency of particle orientation search by developing a parallel algorithm that performs progressively localized projection matching, developing techniques for enhancing the convergence and robustness of 3-D reconstruction, seeking alternative local optimization algorithms to simultaneously correct both the particle orientation parameters and the 3-D structure, and combining the local optimization scheme with a global combinatorial search strategy to be developed in Project E. In addition to designing and implementing new algorithms, we will also develop strategies for highly parallel computation to speed up the compute-intensive tasks in single particle reconstructions. These will include optimal data decomposition, task scheduling, and techniques for exploiting memory hierarchy and reducing I/O latency. In all of the areas of this research, we will interact and collaborate closely with investigators from the other projects in this Program, the primary goal being to ensure that the results of our work are integrated into SPIDER (Project A), SPARX (Project B), software technology that is developed for automated boxing of images of single particles (Project D), and software technology that is developed for global optimization of inter-particle alignment parameters (Project E).

这个项目的目标是填补低温电子研究发展之间的空白。 我们将继续利用显微技术(低温电子显微镜)和大规模科学计算方面的最新进展，从而更好地进行常规和及时的高分辨率单粒子重建。为了实现这一目标，我们将在设计和实现新的数值算法方面进行研究，这些算法将显著减少执行高分辨率单粒子重建所需的工作量。我们的工作将包括通过开发执行渐进式局部投影匹配的并行算法来提高粒子定向搜索的效率，开发技术来增强粒子定向搜索的效率 除了设计和实现新的算法外，我们还将开发高度并行计算的策略，以加速单粒子重建中的计算密集型任务。这些将包括优化数据分解、任务调度，以及利用内存层次结构和减少I/O延迟的技术。 在本研究的所有领域，我们将与本计划其他项目的研究人员密切互动和合作，主要目标是确保我们的工作成果被整合到SPIDER(项目A)、SPARX(项目B)、为单粒子图像自动装箱开发的软件技术(项目D)以及为粒子间比对参数的全局优化开发的软件技术(项目E)中。