Autostereoscopic Visualization and Geometric Computing for Biological Macromolecules

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NSF 0429753 Autostereoscopic Visualization and Geometric Computing forBiological MacromoleculesThe planned research seeks to develop tools and techniques forvisualization and geometric reasoning for large proteomics data. Theproposed research has three main components -- implicit-function-basedrepresentations for proteins, faster computation of proteinelectrostatics, and real-time auto-stereoscopic visualization. Theresearch will develop a unified framework of implicit functions thatcan compactly and efficiently represent non-bonded protein propertiesincluding protein electrostatics and accessibility. By tightly couplingthese properties, implicit-function-based representations will likelyresult in synergistically favorable crossovers in the solutions to theseproblems. Further, providing adaptive, progressive, and hierarchicalrepresentations for implicit functions will allow them to be used inmultiresolution schemes for storage, transmission, and computationalsteering. Protein electrostatic interactions are of central importance formany biological processes. Autostereoscopic visualization of proteinsand their properties is important in understanding the 3D structure ofproteins. The proposed research will develop new methods for renderingimplicit surfaces and volumes using the recent advances in graphicshardware as well as adapt efficient methods for autostereoscopic displayswith personalization.