Statistical Methods for Differential Network Biology with Applications to Aging

差异网络生物学的统计方法及其在衰老中的应用

• 批准号: 1561814
• 负责人: Ali Shojaie
• 金额: $ 118.89万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561814&HistoricalAwards=false
• 关键词: Statistical; Methods; Differential; Network; Biology

Networks are widely used in molecular biology to model interactions among components of biological systems and gain insight into changes in biological mechanism associated with various diseases. Recent evidence suggests that changes in biological networks, including rewiring or disruption of key interactions, may be associated with the development of complex diseases. This research project is motivated by the study of changes in metabolic networks in association with evolution and aging. The project aims to develop new statistical machine learning methods to determine whether evolutionary changes are manifested through changes in how metabolites interact with each other in metabolic pathways. More broadly, the methodologies under development in this project and the accompanying software will provide novel tools for biomedical researchers to infer differential patterns of connectivity in molecular networks associated with complex diseases. The methodologies under development in this project utilize the framework of graphical models to estimate interactions among components of biological networks and identify changes in such networks associated with evolution and aging. Probabilistic graphical models provide a general framework for modeling interactions among random variables. While recent methodological and theoretical advances have facilitated the applications of graphical models to analysis of high-dimensional biological networks, existing methods are not applicable to heterogeneous and non-Gaussian observations obtained from mass-spectrometry-based metabolomics profiling experiments in complex aging studies. The research project aims to bridge this gap by developing new statistical machine learning methods for learning graphical models from heterogeneous and non-Gaussian observations and inferring changes in graphical models in different subpopulations. In particular, the project will (i) develop a flexible framework for estimation of multiple graphical models from heterogeneous populations with complex structures, (ii) develop an inference framework for detecting differential connectivity in biological networks, and (iii) provide a general framework for estimation of non-Gaussian graphical models, with changes over time or experimental conditions. Together, these tools provide a comprehensive framework for differential network analysis and will advance the current state of statistical machine learning methods for the analysis of high-dimensional biological networks.

网络在分子生物学中被广泛用于模拟生物系统各组成部分之间的相互作用，并深入了解与各种疾病相关的生物机制的变化。最近的证据表明，生物网络的变化，包括关键相互作用的重新连接或中断，可能与复杂疾病的发展有关。这项研究项目的动机是研究与进化和衰老相关的代谢网络的变化。该项目旨在开发新的统计机器学习方法，以确定进化变化是否通过代谢途径中代谢物相互作用的变化来体现。更广泛地说，该项目中正在开发的方法和附带的软件将为生物医学研究人员提供新的工具，以推断与复杂疾病相关的分子网络中不同的连接模式。该项目中正在开发的方法利用图形模型的框架来估计生物网络各组成部分之间的相互作用，并确定这种网络中与进化和衰老有关的变化。概率图形模型为随机变量之间的相互作用建模提供了一个通用框架。虽然最近的方法和理论进步促进了图形模型在高维生物网络分析中的应用，但现有方法不适用于复杂衰老研究中基于质谱学的代谢组谱实验获得的非均匀和非高斯观测。该研究项目旨在通过开发新的统计机器学习方法来弥合这一差距，该方法用于从异质和非高斯观测中学习图形模型，并推断不同子群中图形模型的变化。特别是，该项目将(I)开发一个灵活的框架，用于估计具有复杂结构的不同种群的多个图形模型，(Ii)开发一个用于检测生物网络中差异连通性的推理框架，以及(Iii)提供一个通用框架，用于估计随时间或实验条件的变化的非高斯图形模型。总而言之，这些工具为差异网络分析提供了一个全面的框架，并将推动用于分析高维生物网络的统计机器学习方法的现状。