Better Network Modules: New Tools for Protein Network Analysis

该奖项是根据2009年《美国复苏与再投资法案》(Public Law 111-5)获得资助的。马里兰大学园区分校获得了一笔赠款，用于开发新的算法和一套软件工具，这些算法和软件工具基于对网络模块的通用和灵活定义，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采样蛋白质-蛋白质相互作用，并创造出必须通过计算分析的丰富数据。研究这些网络的一个核心挑战是找到其中具有生物学意义和可解释的模块。新的工具和算法将用于改善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体和生物过程，并从合成的致命相互作用数据中发现冗余路径。它们还将被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生物学家研究特定蛋白质复合体和途径的能力，以更好地利用嘈杂的网络数据，而拟议的可视化软件将极大地提高研究人员的水平？能够以交互方式探索这些网络。将创建一个公共数据库，以管理该项目和其他项目使用该方法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，它将通过增加可用于分析相互作用数据的信息技术基础设施、在计算生物学家和生物学家之间更好地传递假说以及通过在暑期实习计划中培训本科生来产生更广泛的影响。有关该项目以及如何访问数据库和软件的信息将在项目网站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/.