GRID COMPUTING & ANALYSIS FOR MULTI SCALE BIOMEDICAL APPLICATIONS

这个子项目是许多利用 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。 生物学和生物医学研究的数据非常丰富，社区数据库（如蛋白质数据库）在几个不同的规模。此外，通过微阵列数据、高分辨率电子显微镜和高分辨率磁共振成像扫描仪等高吞吐量技术，数据的生成速度越来越快。 完成生物和生物医学研究或实验数据分析所需的步骤顺序包括从在专用仪器上获取原始数据到将注释数据存入社区数据库。 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，然后重复此循环的某些部分。 新兴的IT支持范式将网格服务、工作流和数据技术结合在一起。 网格计算作为一种透明地访问生物和生物医学信息学应用程序的计算能力和数据集的常规方法正在迅速获得认可。 这些技术在学术界和工业界都得到了应用。 然而，使生物医学代码能够在这样的环境中运行，需要对这些代码和相关的数据存储库进行所谓的“网格启用”。这种将软件程序、数据库和仪器结合在一起以促进工作流程的过程通常涉及软件层的开发。我们将这个网格启用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研究几个典型的生物医学研究项目活动的网格计算需求，并通过利用新兴的网格实践和经验，定义和实现具有适当安全性和访问日志的标准接口。 一旦启用网格，这些代码可以更容易地组合在一起，以创建生物医学分析管道或工作流程。

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.