Information Technology Research (ITR): Next-Generation Bio-Molecular Imaging and Information Discovery

信息技术研究 (ITR)：下一代生物分子成像和信息发现

• 批准号: 0331657
• 负责人: Robert Murphy
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0331657&HistoricalAwards=false
• 关键词: Information; Technology; Research; ITR; Next

This collaborative project brings together a strong multi-institutional interdisciplinary team of investigators to study and advance the current understanding of cellular and sub-cellular events. Continuing technological advances in fluorescence and atomic-force microscopy allow scientists to observe molecular function, distribution, and interrelationships in living cells. However, a full understanding of tens of thousands of proteins and the complex molecular processes they engage in requires a voluminous amount of image data, which currently must be analyzed by visual inspection. To facilitate such an analysis, researchers from the four participating institutions are focusing on three main research thrusts. First, next-generation intelligent imaging involves information processing at the sensor level to enable high-speed and super-resolution imaging. The goal is to enable biologists to study cellular processes at resolutions in time and space that are not possible with current technologies. The second research thrust is pattern recognition and data mining as applied to bio-molecular image collections. Salient features that characterize the underlying patterns in cells and tissues need to be computed for the vast volumes of images acquired through automated microscopy. Third, a distributed database of bio-molecular images is being created. The merging of pattern-recognition and data-mining tools with new, powerful methods for indexing, data modeling, and collaboration, is aimed at creating a unique infrastructure that greatly facilitates image bioinformatics, thus complementing recent revolutionary advances in genomics. The outcome of this research will lead to new and novel information-processing methods for bio-molecular image data. Efficient and effective representation of such data will enable researchers to search and browse through large collections of image and video data and look for similar patterns in such datasets, thus facilitating information discovery. During its five-year duration, this project will develop, test, and deploy a distributed database of bio-molecular image data accessible to researchers around the world. The impact of the distributed database will be through large-scale biology in which the results of a single experiment can be globally correlated with the results from other groups of scientists, thus accelerating discovery of dynamic relationships between structure and function in complex biological systems.The project will develop new courses, and will facilitate student exchanges, semi-annual meetings, and workshops, benefiting students at all levels. This project will train a new generation of biologists, computer scientists and engineers well versed in the imaging and information-processing sciences at the forefront of next-generation biotechnology. Partnership will be established with institutions with large populations of students from groups underrepresented in science and engineering, such as the California State Universities at Fresno and San Bernardino and the Universidad Metropolitan in Puerto Rico, for undergraduate recruitment and outreach. An effective mode of outreach for students is to educate their teachers, and the project will offer summer fellowships for elementary, high-school, college, and university teachers.

这个合作项目汇集了一个强大的多机构跨学科研究团队，以研究和推进当前对细胞和亚细胞事件的理解。荧光和原子力显微镜技术的不断进步使科学家能够观察活细胞中的分子功能，分布和相互关系。然而，要全面了解成千上万的蛋白质及其参与的复杂分子过程，需要大量的图像数据，目前必须通过目视检查进行分析。为了便于进行这种分析，来自四个参与机构的研究人员集中在三个主要研究方向上。首先，下一代智能成像涉及传感器级别的信息处理，以实现高速和超分辨率成像。其目标是使生物学家能够以当前技术无法实现的时间和空间分辨率研究细胞过程。第二个研究重点是模式识别和数据挖掘应用于生物分子图像采集。通过自动显微镜获取的大量图像需要计算表征细胞和组织中潜在模式的显著特征。第三，正在建立一个分布式生物分子图像数据库。将模式识别和数据挖掘工具与新的强大的索引、数据建模和协作方法相结合，旨在创建一个独特的基础设施，极大地促进图像生物信息学，从而补充基因组学最近的革命性进展。这项研究的结果将导致新的和新颖的生物分子图像数据的信息处理方法。这些数据的高效和有效的表示将使研究人员能够搜索和浏览大量的图像和视频数据，并在这些数据集中寻找类似的模式，从而促进信息发现。 在其五年的时间里，该项目将开发、测试和部署一个分布式生物分子图像数据库，供世界各地的研究人员访问。分布式数据库的影响将通过大规模生物学实现，其中单个实验的结果可以与其他科学家小组的结果在全球范围内相关联，从而加速发现复杂生物系统中结构和功能之间的动态关系。该项目将开发新课程，并将促进学生交流，半年度会议和研讨会，使各级学生受益。该项目将培养新一代的生物学家、计算机科学家和工程师，他们精通下一代生物技术前沿的成像和信息处理科学。 将与拥有大量理工科学生的机构建立伙伴关系，如弗雷斯诺和圣贝纳迪诺的加州州立大学以及波多黎各的大都会大学，以招收本科生和扩大招生范围。一个有效的学生推广模式是教育他们的教师，该项目将为小学、中学、学院和大学教师提供暑期研究金。