Bayesian Methods for Protein Fibrillization: Model Integration and Network Dynamics

该项目的重点是发展有原则的统计方法，以了解淀粉样蛋白原纤维的形成和生长，具有广泛的功能和疾病相关生物学相关性的蛋白质聚集体。蛋白质纤维化是一种基本的生物物理现象，是社会广泛关注的问题的基础。这些问题包括老年痴呆症、白内障和II型糖尿病等在我们的老龄化人口中日益普遍的疾病，以及与牛和其他非人类动物的朊病毒疾病有关的经济成本和粮食安全问题。拟议的研究有可能为寻找解决这些严重社会问题的办法提供信息，从而节省经济并改善个人生活。通过开发新的预测和数据分析技术，并使用新的实验数据对其进行验证，该项目将促进我们对增强或抑制蛋白质纤维化因素的理解，同时也产生可以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，结合新颖的统计方法，编程和实验技术。该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，使我们能够以新颖的方式解决生物问题。本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，其中集成的预测将从多个，潜在的非统计模型中获得，在很少或没有测试数据的情况下。其次，该项目将为纤成动力学开发新的模型家族，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在数小时至数天的时间尺度上的相互作用。建模工作将结合现有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有关的现象（特别是在高通量环境中），并用于建模成纤维过程本身的动力学。该研究还将为复杂依赖现象的贝叶斯整合预测模型提供新方法，为大规模动态网络模型提供新的贝叶斯推理、模型选择和模拟技术，并为蛋白质纤化提供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.