NSF-BSF: Computational Methods for Shape Space Analysis in Structural Biology

NSF-BSF：结构生物学中形状空间分析的计算方法

• 批准号: 2309782
• 负责人: Joseph Kileel
• 金额: $ 27.5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309782&HistoricalAwards=false
• 关键词: NSF; BSF; Computational; Methods; Shape

Modern scientific applications require analyzing massive and complex datasets. A prevalent situation is that each data point is itself geometrical (due to being an image for example), while the totality of the dataset carries shape structure as well (due to underlying motion for instance). Consider structural biology, where experimental techniques known as cryo-electron microscopy and X-ray free electron lasers can be used to capture hundreds of thousands of noisy images of a protein of interest. Scientists then use software tools to reconstruct the three-dimensional shape of the protein and its conformations, which are vital to basic science and drug discovery. In this project new computational and mathematical methods will be developed for analyzing image sets and volumetric datasets which exhibit continuous variability. The project will provide graduate student training, and opportunities for students in the US and Israel to collaborate. The first part of the research will integrate metrics from the field of optimal transport with machine learning methods for dimensionality reduction and clustering, in a way that is tailored towards the analysis of shape space datasets. Graph-based methods will be combined with the Wasserstein metric with emphases on noise robustness, sample and computational efficiency, and interpretability in terms of geometric deformations. The second part of the research will be to develop an algebraic framework for dealing with symmetries in shape space datasets, such as rotational symmetries. Group representation theory will be used within principal component analysis and graph-based learning methods to achieve better efficiency than current approaches. This project will produce rigorous mathematical algorithms with broad applicability, and specialized software libraries for pressing scientific applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代科学应用需要分析大量和复杂的数据集。 一个普遍的情况是，每个数据点本身都是几何（由于是图像的原因），而数据集的总体也带有形状结构（例如，由于基础运动）。 考虑结构生物学，其中称为冷冻电子显微镜和X射线无电子激光器的实验技术可用于捕获成千上万的感兴趣蛋白质的嘈杂图像。 然后，科学家使用软件工具来重建蛋白质的三维形状及其构象，这对于基础科学和药物发现至关重要。 在此项目中，将开发新的计算方法和数学方法，以分析表现出连续变异性的图像集和体积数据集。 该项目将为美国和以色列的学生提供合作的研究生培训和机会。研究的第一部分将以最佳传输领域的指标与机器学习方法的降低和聚类的方式集成，以量化形状空间数据集的分析方式。 基于图的方法将与Wasserstein指标相结合，重点是噪声稳健性，样品和计算效率，以及在几何变形方面的解释性。 研究的第二部分将是开发一个代数框架，用于处理形状空间数据集中的对称性，例如旋转对称性。 小组表示理论将用于主成分分析和基于图的学​​习方法中，以比当前的方法实现更好的效率。 该项目将生产具有广泛适用性的严格数学算法，并专门用于按下科学应用程序。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响评估标准的评估来通过评估来获得支持的。