Quantitative Insight into Gene Cooperation

基因合作的定量洞察

• 批准号: 7214135
• 负责人: ANDREI Y YAKOVLEV
• 金额: $ 30.03万
• 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/7214135
• 关键词: Area; Biological; Candidate Disease Gene; Cell Proliferation; Cell physiology; Cells; Characteristics; Complex; Computational Biology; Data; Dissection; Drug Delivery Systems; Gene Expression; Genes; Methodology; Methods; Mutation; Oncogenes; Oncogenic; Outcome; Proteins; Regulator Genes; Regulatory Pathway; Research; Signal Transduction; Signaling Molecule; Supporting Cell; analytical tool; insight; novel; novel strategies; programs; reconstruction; research study; response; statistics

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）： 该研究的目的是为在基因表达水平上分析基因产物之间的复杂相互作用提供新的方法。大量研究表明，细胞暴露于两种不同信号的结果无法从对每种信号的反应中预测。这一一般原则适用于外源信号分子的协同作用以及基因表达和突变的影响。详细分析这种相互作用的后果是创建的机制，需要新的方法来解剖基因调控网络。作为最详细的分析不同的监管途径之间的相互作用已经进行了合作的癌基因的表达的后果方面，这一领域是特别有前途的新开发的分析工具的应用。拟议的方法包括以下组成部分：（1）多变量搜索一组差异表达基因，允许基因表达数据的多维特征，（2）鉴定参与细胞对多个突变的协同应答的候选基因的子集，和（3）重建基因信号网络，该网络是多个突变对特定细胞功能的协同作用的基础，从而提供关于合作基因之间的因果关系的必要信息。通过结合计算生物学，多元统计和实验研究的方法，可以大大提高理解细胞对多个基因扰动的反应的复杂机制的能力。虽然主要目标是开发一种通用的方法，将广泛适用于各种实验环境，生物学实验将进行研究的影响，两个致癌突变等基本细胞功能的生存和增殖。该实验计划可能具有额外的益处，例如在支持细胞增殖和/或存活的特定回路内精确定位药物靶标。