Autostereoscopic Visualization and Geometric Computing for Biological Macromolecules

NSF 0429753生物大分子的自动立体可视化和几何计算计划的研究旨在开发用于大型蛋白质组学数据的可视化和几何推理的工具和技术。这项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。该研究将建立一个统一的隐式函数框架，可以有效地表示蛋白质的非键合性质，包括蛋白质的静电和可及性。通过紧密耦合这些属性，基于隐函数的表示将可能在这些问题的解决方案中产生协同的有利交叉。此外，为隐函数提供自适应、渐进和分层表示将允许它们用于存储、传输和计算控制的多分辨率方案。蛋白质间的静电相互作用在许多生物过程中具有重要意义。 蛋白质及其性质的自动立体显示对于理解蛋白质的三维结构具有重要意义。拟议的研究将开发新的方法来渲染隐式表面和体积使用graphicshardware的最新进展，以及适应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.