生物代谢网络融合遗传风险的复杂疾病特异代谢通路识别研究

项目名称： 生物代谢网络融合遗传风险的复杂疾病特异代谢通路识别研究

项目编号： No.61272388

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 陈丽娜

作者单位： 哈尔滨医科大学

项目金额： 60万元

中文摘要： 复杂疾病相关的风险基因、蛋白质、代谢物，及由它们直接影响的具有特异功能划分的生物分子反应级联互作关系构成了复杂疾病特异代谢通路。识别复杂疾病特异代谢通路，可以揭示疾病基因致病机制，指导挖掘疾病相关分子靶点及确立有效的治疗方案。目前，疾病相关通路挖掘方法一般只考察单一通路与疾病之间的关联程度，基于单通路的方法往往不能获得生物代谢背景中多个生物学过程之间的调控途径及影响程度。此外，鉴于复杂疾病风险基因对疾病的贡献和风险权重的不同，基于遗传加权的生物代谢网络背景能更全面地揭示疾病风险基因致病机制。本项目拟整合现有代谢通路数据资源，并融合生物分子遗传因素，采用所提出的遗传风险加权新方法，构建遗传风险加权的整合的生物代谢背景网络，探索复杂疾病特异代谢通路识别方法，构建复杂疾病风险基因作用途径分析平台。目前，该方面的研究尚未见报道。本项目的研究与实施将为复杂疾病致病机制的探索提供一种新途径。

中文关键词： 整合分析；致病基因；人类代谢网络；风险通路；新一代测序技术

英文摘要： Complex diseases are assumed to be associated with risk genes, proteins and metabolites. Disease risk biological molecules and possible cascade reactions disorders with specific functions involved by these biological molecules constitute the disease specific metabolic pathway. Identifying disease specific pathway could not only provide insights into the pathogenesis of human disease but also give some essential guidance for drug targets identification and effective therapy resolutions. Nowadays, disease-related pathway methods focused solely on the association between single pathway and this disease. These 'single pathway-based' methods often lack of more detailed interactions between different biological processes to decipher the pathogenesis of disease under the background of metabolic networks. Due to different contributions or risk values of disease risk genes, it is very likely to take the genetic factors and evaluate their discriminative risk contributions into account when tackling with the high-throughput datasets. In this project, we plan to integrate up-to-date data resources of metabolic pathways, evaluate genetic risk values of biological molecules and then introduce a novel and integrated method to identify the disease specific pathway. And meanwhile, a web server and analytic platform will be estab

英文关键词： integrative analysis；Pathogenic gene；Human metabolic network；risk pathway；next generation sequencing