Bayesian Methods for Protein Fibrillization: Model Integration and Network Dynamics

该项目的中心是开发原则性统计方法，以了解淀粉样纤维的形成和生长，淀粉样纤维是具有广泛功能和与疾病相关的生物学意义的蛋白质聚集体。蛋白质纤化是一种基本的生物物理现象，是引起巨大社会关注的问题的基础。这些疾病包括阿尔茨海默氏症、白内障和II型糖尿病，这些疾病在我们老龄化的人口中越来越普遍，以及与牛和其他非人类动物的Pron疾病有关的经济成本和食品安全问题。拟议的研究有可能为寻找这些严重社会问题的解决方案提供信息，从而既节省经济成本，又改善个人生活。通过开发新的预测和数据分析技术并用新的实验数据验证它们，该项目将促进我们对促进或抑制蛋白质纤化的因素的理解，同时还将产生可潜在应用于其他问题领域的统计创新。该项目还将为研究生和本科生提供独特的跨学科培训计划，结合新的统计方法、编程和实验技术。研究项目将数学社会科学的建模技术与生物物理化学的理论和实验方法相结合，使我们能够以新的方式处理生物学问题。该项目的技术创新主要集中在两个方面。首先，它将开发贝叶斯模型集成的新方法，在测试数据很少或没有测试数据的情况下，将从多个潜在的非统计模型获得集成预测。其次，该项目将开发新的纤化动力学模型家族，扩展最初为社交网络开发的方法，以捕获溶液中单个蛋白质之间的相互作用，时间尺度从几个小时到几天。建模工作将结合现有的实验数据和研究小组收集的生物物理数据进行验证。这项研究将产生新的贝叶斯技术，用于预测与蛋白质聚集相关的现象(特别是在高通量环境中)，并对纤化过程本身的动力学进行建模。这项研究还将带来复杂依赖现象预测模型的贝叶斯集成的新方法、新的贝叶斯推理、模型选择和大规模动态网络模型的模拟技术，以及大量关于蛋白质纤化的生物学相关的经验数据。

This project centers on the development of principled statistical methods for understanding the formation and growth of amyloid fibrils, protein aggregates with broad functional and disease-related biological relevance. Protein fibrillization is a basic biophysical phenomenon that underlies problems of immense social concern. These include diseases such as Alzheimer's, cataract, and type II diabetes that are increasingly prevalent in our aging population, as well as economic costs and food security concerns related to prion disease in cattle and other non-human animals. The proposed research has the potential to inform the search for solutions to these serious societal problems, resulting in both economic savings and improvements in individual lives. By developing new predictive and data analytic techniques and validating them with novel experimental data, the project will advance our understanding of the factors that enhance or inhibit protein fibrillization while also producing statistical innovations that can be potentially applied to other problem domains. This project will also provide a unique interdisciplinary training program for graduate and undergraduate students, incorporating novel statistical methods, programming, and experimental techniques.The research project 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. The technical innovations of this project are focused on two areas. First, it will develop new approaches to Bayesian model integration in which integrated predictions will be obtained from multiple, potentially non-statistical models in cases with little or no test data. Second, the project will develop novel model families for fibrillization kinetics, extending methods originally developed for social networks to capture interactions between individual proteins in solution over time scales of hours to days. The modeling work will be validated by combination of existing experimental data and by biophysical data collected by the research team. The research will result in new Bayesian techniques for predicting phenomena related to protein aggregation (especially in a high-throughput setting), and for modeling the kinetics of fibrillization process itself. The proposed research will also lead to novel methods for Bayesian integration of predictive models for phenomena with complex dependence, new Bayesian inference, model selection, and simulation techniques for large-scale dynamic network models, and a body of biologically relevant empirical data on protein fibrillization.