Computational and Statistical Studies for Multiple Molecular Networks

描述（由申请人提供）：高通量生物技术产生了大量多样的分子网络，包括蛋白质相互作用网络、基因共表达网络和调控网络。网络生物学是一个新兴的领域，旨在通过分子网络来理解基本的生物机制和疾病过程。因此，迫切需要计算和统计工具来从多个网络中挖掘生物知识。然而，这样的计算算法很少，而且几乎没有统计方法被开发用于多网络分析。研究者假设：1)基因子网络的有效得分函数可以被定义，使得高分与生物显著性相关；2)生物网络的统计显著性在数学上是可处理的；3)有效的计算工具可以被开发出来，以发现生物网络中统计显著的模式。这个应用程序的目标就是解决这些问题。此外，研究人员将开发实现这些程序所需的软件。作为实际应用，为了更好地理解与衰老相关的分子网络，这些算法将被用于分析大量与衰老相关的基因表达数据集。研究人员将通过以下具体目标实现所有这些目标：1)为网络模块定义新的评分函数，同时考虑节点度（节点的链接数）和边缘传递性（形成三角形的链接之间的依赖关系）；并开发高效的计算算法来识别高分分子模块；2)建立严格的理论来评估鉴定的分子模块的统计显著性；3)应用完全开发的工具来分析大量与衰老相关的数据集，并在酵母中实验测试预测的子集。大量的网络，它们的规模和复杂性，共同使这成为一个特别具有挑战性的项目。这项研究的结果对大规模网络分析非常有用，因此对生物学的系统理解也非常有用。公共卫生相关性：识别与疾病或药物治疗相关的遗传子网络是生物医学研究中一个重要的具有挑战性的问题。在此应用程序中开发的用于分析多个网络的统计和计算工具将对这项工作至关重要。这些工具将用于识别特定于衰老的基因网络。

DESCRIPTION (provided by applicant): High-throughput biotechnologies have generated a large number and variety of molecular networks, including protein interaction networks, gene coexpression networks, and regulatory networks. Network biology is an emerging field aiming to understand basic biological mechanisms and disease processes by using molecular networks. Therefore, computational and statistical tools are urgently needed to mine biological knowledge from multiple networks. However, few such computational algorithms are available, and almost no statistical methods have been developed for multiple network analysis. The investigators hypothesize 1) that efficient score functions for gene subnetworks can be defined so that high score correlates with biological significance, 2) that the statistical significance of biological networks are mathematically tractable, and 3) that efficient computational tools can be developed to find statistically significant patterns in biological networks. The objective of this application is to address these questions. In addition, the researchers will develop the software necessary to implement these programs. As a practical application, and to gain an understanding of molecular networks involved in aging, these algorithms will be implemented to analyze a large collection of aging-related gene expression datasets. The investigators will achieve all of these objectives through the following specific aims: 1) define novel scoring functions for network modules, taking both node degrees (the number of links of a node) and edge transitivity (the dependency between links forming triangles) into consideration; and develop efficient computational algorithms to identify molecular modules with high scores; 2) develop a rigorous theory to evaluate the statistical significance of the identified molecular modules; and 3) apply the fully developed tools to analyze a large collection of aging-related datasets and experimentally test a subset of the predictions in yeast. The large number of networks, their size, and their complexity, together make this an especially challenging project. The results from this research can be extremely useful for large scale network analysis, and therefore for the systematic understanding of biology. PUBLIC HEALTH RELEVANCE: Identifying genetic subnetworks related to diseases or drug treatments is an important challenging problem in biomedical research. The statistical and computational tools developed in this application for the analysis of multiple networks will be essential for the effort. The tools will be used to identify genetic networks specific to aging.