Computational and Statistical Studies for Multiple Molecular Networks

多分子网络的计算和统计研究

• 批准号: 7532746
• 负责人: Fengzhu Sun
• 金额: $ 16.71万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7532746
• 关键词: Address; Aging; Algorithms; Animal Model; Biological; Biology; Biomedical Research; Biotechnology; Characteristics; Classification; Collection; Computational algorithm; Computing Methodologies; Data; Data Set; Dependence; Dependency; Development; Disease; Gene Expression; Genes; Genetic; Graph; Internet; Knowledge; Laboratories; Link; Methods; Mining; Molecular; Numbers; Pathway Analysis; Pathway interactions; Pattern; Pharmaceutical Preparations; Process; Proteins; Public Health; Range; Research; Research Personnel; Score; Software Tools; Source Code; Statistical Methods; Statistical Study; Statistically Significant; Structure; Testing; Yeasts; age related; base; computerized tools; design; gene function; high throughput technology; network models; novel; open source; practical application; programs; simulation; size; software development; theories; tool

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.

描述(申请人提供)：高通量生物技术已经产生了大量和多样化的分子网络，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了解基本的生物学机制和疾病过程。因此，迫切需要计算和统计工具来从多个网络中挖掘生物学知识。然而，很少有这样的计算算法可用，而且几乎没有开发出用于多网络分析的统计方法。研究人员假设：1)可以定义有效的基因子网络得分函数，以便高分与生物学意义相关；2)生物网络的统计意义在数学上是容易处理的；3)可以开发有效的计算工具来发现生物网络中具有统计意义的模式。本应用程序的目标就是解决这些问题。此外，研究人员还将开发实施这些计划所需的软件。作为一个实际应用，为了了解涉及衰老的分子网络，这些算法将被实施来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)和边传递性(形成三角形的链接之间的相关性)；并开发高效的计算算法来识别高分数的分子模块；2)开发严格的理论来评估已识别的分子模块的统计意义；以及3)应用完全开发的工具来分析大量与衰老相关的数据集，并在酵母中对预测的子集进行实验测试。大量的网络、其规模和复杂性共同构成了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：在生物医学研究中，识别与疾病或药物治疗相关的遗传子网络是一个重要的挑战性问题。在这一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。这些工具将被用来识别特定于衰老的遗传网络。