Quantitative Insight into Gene Cooperation

基因合作的定量洞察

• 批准号: 7035783
• 负责人: ANDREI Y YAKOVLEV
• 金额: $ 30.54万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7035783
• 关键词: computational biology; gene expression; gene interaction; gene mutation; genetic regulation; mathematical model; model design /development; neoplastic transformation; oncogenes; polymerase chain reaction; tissue /cell culture

DESCRIPTION (provided by applicant): The objectives of the proposed research are to provide novel methods for the analysis of the complex interactions between gene products at the level of gene expression. A great deal of research demonstrates that the outcome of exposure of a cell to two different signals is not predictable from the response to each signal on its own. This general principle holds true for the cooperative effects of exogenous signaling molecules as well as the effects of gene expression and mutation. Detailed analysis of the mechanisms by which the consequences of such interactions are created requires novel approaches to the dissection of gene regulatory networks. As the most detailed analysis of interactions between different regulatory pathways has been carried out in regard to the consequences of expression of cooperating oncogenes, this area is especially promising for the application of newly developed analytical tools. The proposed methodology includes the following components: (1) Multivariate search for a set of differentially expressed genes allowing for multidimensional characteristics of gene expression data, (2) Identification of a subset of candidate genes that participate in the cooperative response of a cell to multiple mutations, and (3) Reconstruction of a gene signaling network that underlies the cooperative effect of multiple mutations on a specific cell function, thereby providing the necessary information on causal relationships between cooperating genes. By combining methods of computational biology, multivariate statistics, and experimental studies it is possible to greatly enhance the ability to understand complex mechanisms of cell responses to multiple gene perturbations. While the main objective is to develop a general methodology that will be widely applicable in various experimental settings, biological experiments will be conducted to study the effects of two oncogenic mutations on such fundamental cell functions as survival and proliferation. This experimental program may have additional benefits such as pinpointing drug targets within a particular circuit supporting cell proliferation and/or survival.

描述（由申请人提供）：拟议研究的目标是在基因表达水平上为基因产物之间复杂相互作用的分析提供新的方法。大量的研究表明，细胞暴露于两种不同信号的结果是不能从对每个信号的反应来预测的。这一普遍原则适用于外源信号分子的协同效应以及基因表达和突变的影响。对这种相互作用产生的后果的机制的详细分析需要新的方法来解剖基因调控网络。由于对不同调控途径之间的相互作用进行了最详细的分析，并对合作癌基因表达的后果进行了分析，这一领域特别有希望应用新开发的分析工具。拟议的方法包括以下组成部分：(1)对一组差异表达基因进行多变量搜索，从而获得基因表达数据的多维特征；(2)确定参与细胞对多个突变的协同反应的候选基因子集；(3)重建一个基因信号网络，该网络是多个突变对特定细胞功能协同效应的基础。从而为合作基因之间的因果关系提供必要的信息。通过结合计算生物学、多元统计学和实验研究的方法，可以大大提高理解细胞对多基因扰动反应的复杂机制的能力。虽然主要目标是开发一种将广泛适用于各种实验环境的通用方法，但将进行生物学实验来研究两种致癌突变对诸如生存和增殖等基本细胞功能的影响。这个实验程序可能有额外的好处，例如在支持细胞增殖和/或存活的特定电路中精确定位药物靶点。