CAREER: Sparse Spatial Reasoning for High-Throughput Protein Structure Determination

职业：用于高通量蛋白质结构测定的稀疏空间推理

• 批准号: 0237654
• 负责人: Christopher Bailey-Kellogg
• 金额: $ 48.81万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0237654&HistoricalAwards=false
• 关键词: CAREER; Sparse; Spatial; Reasoning; Throughput; CAREER; Sparse; Spatial; Reasoning; Throughput

This is a Faculty Early Career Development (CAREER) award. The research will develop new methods for analyzing the structure of protein molecules, interpreting spatial data sets containing significant noise and sparse information content. Despite many experimental and computational advances, traditional structure determination protocols remain very difficult, expensive, and time-consuming. Consequently, in order to increase the throughput of structure determination, researchers are pursuing minimalist techniques that provide much less structure information much faster; examples include mutation studies, indicating at which positions amino acid substitutions significantly affect the protein's function; cross-linking mass spectrometry, providing crude proximity information for some positions in the protein; and electron microscopy, elucidating the protein's surface/volume at relatively low resolution. These minimalist experiments then place more burden on associated algorithms for experiment planning and data interpretation.This project pursues new theory, representations, and algorithms to address data interpretation and experiment design problems in domains characterized by sparse spatial data. A significant component of the research is the case study application of minimalist protein structure determination. The education plan addresses the need to build bridges between computer science and the life sciences in order to attack problems of this combined computational-experimental kind.A spatial reasoner will be developed, leveraging key problem structure to efficiently and effectively plan and interpret experiments. It will represent data, models, and biophysical knowledge with multi-level, multi-dimensional topological and geometric objects and constraints. This representation will allow algorithms to match features of data and models, overcome problems of noise and scarcity by uncovering consistent feature sets, target clarifying queries in response to conflicts, and plan additional experiments. This approach will thus support closed-loop integration of modeling and experiment -- experimental evidence will trigger evaluation of model features and even optimization of models themselves, while model analysis will trigger specific data interpretation questions and even new experiments.The education component of this project brings together students from computer science and the life sciences to train them for interdisciplinary computational biology research. Additional and revised coursework, to be developed in conjunction with the Computer Science Department and Computational Science and Engineering program at Purdue, will combine advanced computational techniques and biological applications. The training will provide life science students with the necessary algorithmic background and computer science students with the necessary exposure to and experience with motivating biological problems. Research opportunities, course projects, and other learning opportunities will further involve students in the many challenging and fascinating biological problems requiring advanced computational techniques.This CAREER award recognizes and supports the early career-development activities of a teacher-scholar who is likely to become an academic leader of the twenty-first century. The research will lead to scientific contributions in the structural and functional understanding of biomolecular machinery. The challenges faced in developing, applying, and extending algorithms for this application will lead to core contributions in reasoning about physical systems, where many similar tasks in planning, modeling, predicting, and controlling face similar problems with sparse, noisy spatial data.

这是教师早期职业发展（职业）奖。 该研究将开发出分析蛋白质分子结构的新方法，解释包含明显噪声和稀疏信息含量的空间数据集。 尽管有许多实验和计算进步，但传统的结构确定协议仍然非常困难，昂贵且耗时。 因此，为了增加确定结构的吞吐量，研究人员正在追求简约的技术，这些技术提供了更少的结构信息。例子包括突变研究，表明在哪些位置氨基酸取代显着影响蛋白质的功能。交联的质谱法，为蛋白质中某些位置提供粗略的接近信息；和电子显微镜，以相对较低的分辨率阐明了蛋白质的表面/体积。 然后，这些极简主义实验对实验计划和数据解释的相关算法增加负担。本项目追求新的理论，表示和算法来解决以稀疏空间数据为特征的域中数据解释和实验设计问题。 该研究的重要组成部分是案例研究的应用，对极简主义蛋白质结构的确定确定。 教育计划解决了需要在计算机科学和生命科学之间建造桥梁以攻击这种合并的计算实验类型的问题。将开发空间推理器，利用关键问题结构来有效地计划和解释实验。 它将用多层次，多维拓扑和几何对象和约束代表数据，模型和生物物理知识。 该表示形式将允许算法匹配数据和模型的功能，克服噪声和稀缺问题，通过发现一致的功能集，目标澄清对冲突的响应查询以及计划其他实验。 因此，这种方法将支持建模和实验的闭环整合 - 实验证据将触发模型特征的评估，甚至对模型本身的优化，而模型分析将触发特定的数据解释问题，甚至是新的实验。该项目的教育组成部分将计算机科学和生命科学的学生聚集在一起，以培训他们的跨学科计算生物学研究。与普渡大学的计算机科学系与计算科学与工程计划一起开发的其他和修订的课程将结合高级计算技术和生物应用。该培训将为生命科学专业的学生提供必要的算法背景和计算机科学专业的学生，​​并在激励生物学问题上接触和经验。 研究机会，课程项目和其他学习机会将进一步使学生参与需要高级计算技术的许多具有挑战性和迷人的生物学问题。该职业奖会认可并支持教师 - cholar的早期职业发展活动，他们可能成为二十一世纪的学术领导者。 这项研究将导致对生物分子机械的结构和功能理解的科学贡献。 开发，应用和扩展该应用程序算法面临的挑战将导致有关物理系统推理的核心贡献，在这些系统中，在计划，建模，预测和控制方面许多类似的任务与稀疏，嘈杂的空间数据相似。