DMS/NIGMS 2: Statistical Network Models for Protein Aggregation

DMS/NIGMS 2：蛋白质聚集的统计网络模型

• 批准号: 10378277
• 负责人: Carter Tribley Butts
• 金额: $ 29.91万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10378277
• 关键词: Address; Alzheimer' s Disease; Amyloid Fibrils; Biological; Biological Process; Biophysics; Cataract; Collaborations; Collection; Complex; Coupling; Crystalline Lens; Crystallins; Data; Data Collection; Development; Disease; Etiology; Food Safety; Heat shock proteins; Hour; Individual; Mathematics; Medical; Methods; Modeling; Molecular Chaperones; National Institute of General Medical Sciences; Non-Insulin-Dependent Diabetes Mellitus; Pathway Analysis; Pathway interactions; Prion Diseases; Proteins; Research; Scientist; Social Network; Social Sciences; Speed; Structure; System; Techniques; Tertiary Protein Structure; Testing; Time; Work; aging population; alpha-Crystallins; amyloid fibril formation; biophysical chemistry; biophysical techniques; experimental study; innovation; insight; model development; network models; novel; protein aggregation; protein structure function; social; statistics

This project centers on the development of statistical network models for understanding the formation of protein aggregates associated with disease states as well as critical biological processes. Systems of this type include amyloid fibrils and toxic oligomers, amorphous protein aggregates, and the large, dynamic complexes formed by small heat shock proteins. Our work combines modeling techniques from the mathematical social sciences with theoretical and experimental methods from biophysical chemistry, enabling us to approach biological problems in novel ways. Our technical innovations are focused on Hamiltonian-driven network models, extending methods originally developed for social networks to capture interactions among individual proteins in solution over time scales of hours to days. The project team comprises an established collaboration between a mathematical social scientist and statistician with expertise in computational statistics and network analysis, and an experimental biophysical chemist with relevant expertise in protein structure and function. Essential components of this research include both the creation of modeling techniques that can be used effectively with existing experimental data, and the collection of new data to validate our modeling work. This work will result in a collection of novel methods for the study of protein aggregation that are both statistically principled and empirically grounded, as well as biologically relevant empirical data.

该项目的重点是开发统计网络模型，以了解 与疾病状态以及关键生物过程相关的蛋白质聚集体。这个系统 类型包括淀粉样原纤维和有毒低聚物、无定形蛋白聚集体以及大的、动态的 由小的热休克蛋白形成的复合物。我们的工作结合了来自 数学社会科学与生物物理化学的理论和实验方法， 使我们能够以新颖的方式解决生物学问题。我们的技术创新主要集中在 哈密​​顿驱动的网络模型，扩展了最初为社交网络开发的方法来捕获 溶液中各个蛋白质之间在数小时至数天的时间范围内的相互作用。 该项目团队由数学社会科学家和 统计学家，具有计算统计和网络分析方面的专业知识，以及实验生物物理学 具有蛋白质结构和功能相关专业知识的化学家。本研究的基本组成部分 包括创建可以有效地与现有实验一起使用的建模技术 数据，以及收集新数据来验证我们的建模工作。这项工作将收集 研究蛋白质聚集的新方法，既具有统计学原理，又具有经验性 扎根的以及生物学相关的经验数据。