Systems Biology and Gene Networks

摘要 核心A的主要作用-“系统生物学和基因网络”-是以预测的方式探索 哮喘和COPD之间基于分子和网络的相似性和差异。网络提供 用于从程序项目中的实体之间的一组关系中导出信息的框架。我们的目标 开发工具来识别哮喘和COPD的疾病模块，以及分子关系， 他们之间这项工作最初将依赖于PPG调查人员已经收集的数据，从而导致 第一轮机械预测。这些预测将推动后续实验工作的设计 和数据收集，不断提高核心的预测能力。核心A有两个具体目标：目标1： 构建网络基础设施，以依靠 PPG项目和核心。其目标是发展和改进现有的工具来识别这种疾病 哮喘和慢性阻塞性肺病的模块，并提供一套初步的实验测试的预测。目标二： 通过叠加PPG收集的数据，迭代增强疾病模块的预测值 项目和核心。我们计划通过以下方式探索哮喘和COPD之间基于网络的关系： 确定共同的途径，基因和潜在的共同疾病机制。核心A将应用网络 不同层次的分析支持：（1）选择和优先化与以下相关的变异和基因： 项目1的气流阻塞;（2）整合不同水平的基因组学和转录组学数据 与哮喘和COPD相关的分子相互作用网络（相互作用组）;以及 (3)识别和解释与哮喘相关的表观遗传变化（甲基化和miRNA）， 通过与蛋白质相互作用组模型整合，来自项目3的COPD。互动网络将服务于 作为信息的支架，以提取全局和局部图论属性，以告知初始种子 基因选择和项目1、2和3中不同的“组学”数据。

ABSTRACT The primary role of Core A – “Systems Biology and Gene Networks” — is to explore in a predictive fashion the molecular and network-based similarities and differences between asthma and COPD. Networks provide frameworks for deriving information from a set of relationships among entities in the program project. We aim to develop tools to identify the disease modules for asthma and COPD, and the molecular relationships between them. The work will initially rely on the data already collected by the PPG investigators, leading to a first round of mechanistic predictions. These predictions will drive the design of subsequent experimental work and data collection, iteratively improving the Core's predictive power. Core A has two specific aims: Aim 1: Construct the network infrastructure to predict the disease module relying on data collected by the PPG Projects and Cores. The goal is to develop and improve on the existing tools to identify the disease module for asthma and COPD, and to offer an initial set of experimentally testable predictions. Aim 2: Iteratively enhance the predictive value of the disease module by overlaying data collected by the PPG Projects and Cores. We plan to explore the network-based relationships between asthma and COPD by identifying common pathways, genes and potentially common disease mechanisms. Core A will apply network analysis support at different levels: (1) selection and prioritization of the variants and genes associated with airflow obstruction from Project 1; (2) integrating different levels of genomics and transcriptomics data associated with asthma and COPD with the molecular interaction networks (interactome) from Project 2; and (3) identifying and interpreting epigenetic changes (methylation and miRNA) associated with asthma and COPD from Project 3 by integration with protein interactome models. The networks of interactions will serve as a scaffold for information to extract global and local graph theory properties to inform about the initial seed gene choices and about the different “omics” data in Projects 1, 2 and 3.