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.

National Science Foundation (NSF)
Emerging Frontiers (EF)
Cooperative Agreement (Coop)
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Kamal Shukla
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University of California Santa Barbara
Santa Barbara
United States
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