This infrastructure enhancement award expands the Optical Imaging Laboratory (OIL) at Brigham and Women's Hospital through the development of an image database and image analysis toolbox. The developed database integrates data obtained from multiple imaging instruments as well as an image processing toolbox implemented using Matlab. The database manages the dataflow by interacting with the imaging instruments, metadata, and the toolbox. The data model consists of four parts: (1)the raw image data, (2) image descriptor data, (3) metadata definitions from image computations, and (4) associated metadata definitions concerning cell line, reagents, subjects used in the experiment as well as information from genetics, biology, and experimental design. The toolbox features a modular design and includes a versatile graphic user interface and file input/output. Its careful design facilitates the implementation of existing and new computational techniques.
The results of the project serve as the backbone of a computational infrastructure for a wide variety of biology research that is increasingly relying on optical imaging and computational analysis. Broad impact of the work arises from the fact that the database and the toolbox are available to researchers and students in biology, engineering, and mathematics for research and teaching activities. Undergraduate and graduate students and postdoctoral fellows involved in the work benefit from the training provided by the multi-disciplinary environment.
The intellectual merit of this project includes the development, implementation, and maintenance of computational techniques to facilitate biology research in different fields. Through the entire life of the project, various computational techniques have been developed and implemented to model, process, and analyze optical images acquired in cell biology, neurobiology, development, stem cells, and other applications in biological research. Through the integration of the computational techniques with imaging instrument and data management, the project facilitated the pursuit of new knowledge about biological processes by providing a fast, objective, and interactive platform for the application of computations in biology. The broad impacts of the project include the unique cross-disciplinary environment to promote training and education to students, fellows, and other trainees. Through the project, students and fellows in engineering, biology, physics, and mathematics worked together in collaborative projects that utilize computations to address biological questions. The real-world questions also present opportunities to students to apply their classroom knowledge into practice. In summary it is found that the project not only facilitated our understanding of biological questions in faster and more objective manner but also broaden the knowledge base of many students at graduate and undergraduate schools. Our out-reach activities enhanced the research experience for the students and other trainees. The outcomes of the project include the integration of computational methods with modern optical imaging techniques, the development of a software toolbox and a data repository, a selection of computational algorithms, and joint publications to disseminate our findings. In addition, the project provided the topics and facility to develop several doctoral dissertations and master and undergraduate thesis.
