CDI-Type-II: Computational Challenges in the Discovery and Understanding of Complex Boiological Structures through Multimodal Imaging

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Recent advances in imaging have enabled multimodal/multiscale observations of complex natural systems. Annotating, harvesting, extracting, and correlating the information contained in these vast image volumes is critically dependent on new information-processing tools as well as robust workflow implementation of well-established tools. In cases such as bioimaging, image data comes from different physical samples from different specimens and needs to be statistically harmonized. Though piecewise computational workflows in data collection are often highly automated, little progress has been made in effective and efficient knowledge discovery. The lack of processing and discovery tools to navigate data of such complexity and magnitude is a critical bottleneck.The project's computational efforts focus on a biological system that represents a unique combination of high complexity and accessibility for imaging: the vertebrate retina. The retina has a very complex yet highly structured architecture consisting of an unknown number of repeated neural circuits. Though it has been a focus of intense anatomical and physiological studies for over a century, no complete retinal map exists today. In fact, contrary to common misunderstanding, not even all of the retinal cell types have been discovered, much less mapped into functional circuitry. A retinal map is the critical anatomical ground truth that is needed in building realistic models of the earliest stage in visual processing. Building such a cell map requires advances in several areas, including imaging and molecular marker technologies, statistical pattern recognition, and databases. However, the critical barrier at this time is in analyzing the vast amount of images that will be generated from such a project, from samples coming from different retinal cross sections of different animals, and the need to integrate this information in a statistically robust manner to build a retinal map. It is expected that this project will advance not only the image-based information processing technologies but will also have a significant impact on neuroscience research.The project is interdisciplinary and brings together researchers at the University of Utah and UCSB. Students on the project will get a broad training in retinal neurobiology, computer science and electrical engineering. This project will integrate research and education by introducing the results of the research into courses taught by the PIs on image processing, databases, bioinformatics, and pattern recognition.