DMS/NIGMS 2: Statistical Network Models for Protein Aggregation

这个项目的中心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。这样的系统 类型包括淀粉样纤维和有毒低聚物，无定形蛋白聚集体，以及大的，动态的 由小分子热休克蛋白形成的复合体。我们的工作结合了来自 数理社会科学结合生物物理化学的理论和实验方法， 使我们能够以新的方式处理生物问题。我们的技术创新专注于 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以捕获 在几小时到几天的时间范围内，溶液中单个蛋白质之间的相互作用。 该项目团队由一名数学社会科学家和一名数学社会科学家 在计算统计和网络分析方面有专长的统计学家，以及实验生物物理学 在蛋白质结构和功能方面具有相关专业知识的化学家。这项研究的基本组成部分 包括创建可有效用于现有实验的建模技术 数据，并收集新数据来验证我们的建模工作。这项工作将产生一系列 研究蛋白质聚集的新方法，既有统计原理，又有经验 有根据的，以及与生物相关的经验数据。

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.