Autostereoscopic Visualization and Geometric Computing for Biological Macromolecules

生物大分子的自动立体可视化和几何计算计划中的研究旨在为大型蛋白质组学数据开发可视化和几何推理的工具和技术。提出的研究有三个主要组成部分——基于隐式函数的蛋白质表示，更快的蛋白质静电计算和实时自动立体可视化。研究将开发一个统一的隐式函数框架，可以紧凑有效地表示非键合蛋白质特性，包括蛋白质静电和可及性。通过紧密耦合这些属性，基于隐式函数的表示可能会在这些问题的解决方案中产生协同有利的交叉。此外，为隐式函数提供自适应，渐进和分层表示将允许它们在多分辨率方案中用于存储，传输和计算转向。蛋白质静电相互作用在许多生物过程中起着至关重要的作用。蛋白质及其性质的自立体可视化对于理解蛋白质的三维结构非常重要。该研究将利用图形硬件的最新进展，开发新的方法来渲染隐式表面和体积，并适应具有个性化的自动立体显示的有效方法。

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.