GRID COMPUTING & ANALYSIS FOR MULTI SCALE BIOMEDICAL APPLICATIONS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 生物学和生物医学研究的数据非常丰富，有几个不同规模的社区数据库(如蛋白质数据库)。此外，通过高吞吐量技术，如微阵列数据、高分辨率电子显微镜和高分辨率磁共振成像扫描仪，正在以越来越快的速度产生数据。完成生物和生物医学研究或实验数据分析所需的一系列步骤从在专门仪器上获取原始数据到将注释数据存储在社区数据库中。完整的工作流程需要科学家将来自不同资源的数据汇集在一起，将实验与模拟进行比较，分析数据，将其可视化，然后重复这个周期的某些部分。这一新兴的IT支持模式融合了网格服务、工作流和数据技术。 网格计算作为一种常规方式被迅速接受，可以透明地访问生物和生物医学信息学应用的计算能力和数据集。这些技术在学术界和工业界都得到了应用。然而，要使生物医学代码在这样的环境中运行，需要对这种代码和相关的数据储存库进行所谓的“网格启用”。这种将软件程序、数据库和工具集合在一起以促进工作流程的过程通常涉及软件层的开发。我们将这一启用网格的步骤称为代码和数据库的“包装”。这个核心技术研究和开发项目的工作重点是检查几个示范性生物医学研究项目活动的网格计算需求，并利用新兴的网格实践和经验定义和实施具有适当安全性和访问日志记录的标准接口。一旦启用了网格，这些代码就可以更容易地组合在一起，创建生物医学分析管道或工作流。

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. Biology and biomedical research is very data-rich with community databases (e.g. Protein Data Bank) at several different scales. Furthermore, data are being generated at increasingly faster rates via high through-put technologies, such as micro array data, high-resolution electron microscopes, and high-resolution magnetic resonance imaging scanners. The sequence of steps needed to complete biological and biomedical research or analysis of experimental data spans from taking raw data on a specialized instrument to depositing annotated data in community databases. The complete workflow requires a scientist to bring together data from disparate resources, compare experiment with simulation, analyze data, visualize it, and then repeat some portions of this cycle. The emerging IT support paradigm for this merges grid services, workflows and data technologies. Grid computing is rapidly gaining acceptance as a routine way to transparently access computational power and datasets for biological and biomedical informatics applications. These techniques are being employed in academia as well as in industry. However, enabling biomedical codes to run in such an environment requires so-called "grid-enabling" of such codes and the associated data repositories. This process of bringing software programs, databases and instruments together to then facilitate a workflow generally involves development of a layer of software. We refer to this grid-enabling step as the "wrapping" of codes and databases. The effort in this Core Technological Research and Development project focuses on examining the grid computing requirements of several exemplary biomedical research project activities, and defining and implementing standard interfaces with appropriate security and access logging by leveraging emerging grid practice and experience. Once grid-enabled, such codes can more easily be combined together to create biomedical analysis pipelines, or workflows.