Better Network Modules: New Tools for Protein Network Analysis

“这项奖励是根据2009年美国复苏和再投资法案（公法111-5）资助的。”马里兰大学帕克分校获得了一笔拨款，用于开发新的算法和一套基于“网络模块”的通用和灵活定义的软件工具，以便从嘈杂和不完整的蛋白质-蛋白质相互作用数据中提取有意义的生物集群。最近开发的高通量技术正被用于从许多生物体中取样蛋白质-蛋白质相互作用，并创造了大量必须进行计算分析的数据。研究这些网络的一个核心挑战是在其中找到有生物学意义和可解释的模块。新的工具和算法将用于改进蛋白质相互作用网络的可视化，识别嵌入网络数据中的蛋白质复合物和生物过程，并从合成致命相互作用数据中发现冗余途径。它们还将用于比较几个不同物种的相互作用网络。由此产生的网络分析软件将扩展系统生物学家和研究特定蛋白质复合物和途径的生物学家的能力，以更好地利用噪声网络数据，并且提议的可视化软件将极大地提高研究人员的能力。交互式探索这些网络的能力。一个公共数据库将被创建，用来管理这个项目和其他使用这种方法的项目所做的计算预测注释。这项工作将加强对生物网络组织的理解，并将通过增加可用于交互数据分析的信息技术基础设施，提供计算生物学家和生物学家之间更好的假设传递，以及通过暑期实习计划培训本科生，产生更广泛的影响。有关该项目的信息以及如何访问数据库和软件将在该项目的网站http://www.cbcb.umd.edu/research/bionet/上提供。

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."The University of Maryland College Park is awarded a grant to develop new algorithms and a suite of software tools based on a general and flexible definition of a "network module" in order to extract meaningful biological clusters from noisy and incomplete protein-protein interaction data. Recently developed high-throughput techniques are being used to sample protein-protein interactions from many organisms and are creating a wealth of data that must be analyzed computationally. A central challenge in the study of these networks is finding biologically meaningful and interpretable modules within them. The new tools and algorithms will be used to improve visualization of protein interaction networks, identify protein complexes and biological processes embedded within the network data, and to discover redundant pathways from synthetic lethal interaction data. They will also be applied to comparing the interaction networks of several different species. The resulting network analysis software will expand the capabilities of both systems biologists and biologists working on particular protein complexes and pathways to make better use of noisy network data, and the proposed visualization software will vastly improve researchers? capability to interactively explore these networks. A public database will be created to curate computationally predicted annotations made by this, and other, projects using this method. The work will strengthen understanding of the organization of biological networks, and it will have broader impact by increasing the information technology infrastructure available for the analysis of interaction data, providing better transfer of hypotheses between computational biologists and biologists, and by the training of undergraduates in a summer internship program. Information about the project and how to access the database and software will be available at the project website http://www.cbcb.umd.edu/research/bionet/.