Mapping disease-specific human protein networks

描述（由申请人提供）：虽然人类基因组序列是已知的，但医学进步将需要了解单个基因和蛋白质如何组装成功能性生物网络。这些网络部分是通过蛋白质之间的物理相互作用建立的。蛋白质相互作用网络现在可以使用高通量实验方法进行探测，但挑战仍然存在：数据可能是嘈杂和不完整的，并且为更简单的模型生物开发的方法可能难以扩展到人类。 该提案旨在通过开发一种联合计算/实验方法来应对这些挑战，以确定绘制与疾病相关的人类网络的可行性。具体目标包括开发一个统计指标，用于高通量双杂交筛选的蛋白质组数据的置信度;开发用于跨物种映射网络的计算方法;以及通过计算预测指导的网络探索生成实验数据。 如果这项可行性研究成功，第二阶段的工作将侧重于为两个特定疾病领域建立原理验证网络。示范性领域是癌症，以提示小分子和抗体药物的潜在靶点，以及感染性疾病，以鉴定宿主-病原体相互作用。该项目第一阶段的成功将改进绘制与疾病相关的人类生物网络的方法。第二阶段的成功将导致治疗干预的目标。

DESCRIPTION (provided by applicant): While the human genome sequence is known, medical advances will require knowledge of how individual genes and proteins assemble into functional biological networks. These networks are built in part through physical interactions between proteins. Protein interaction networks can now be probed using highthroughput experimental methods, yet challenges remain: data can be noisy and incomplete, and methods developed for simpler model organisms can be difficult to scale up to human. This proposal aims to determine the feasibility of mapping human networks relevant to disease by developing a joint computational / experimental approach to meeting these challenges. Specific aims include developing a statistical metric for confidence in proteomic data from high-throughput two-hybrid screens; developing computational methods for mapping networks cross-species; and generating experimental data with network exploration guided by the computational predictions. If this feasibility study is successful, work in Phase II will focus on building proof-of-principle networks for two specific disease areas. Exemplary areas are cancer, to suggest potential targets for small-molecule and antibody drugs, and infectious disease, to identify host-pathogen interaction. Success in Phase I of this project will result in improved methods for mapping disease-relevant human biological networks. Success in Phase II will result in targets for therapeutic intervention.