CompBio: Gene Interactions as a Model for Network Architectures

CompBio：基因相互作用作为网络架构模型

• 批准号: 0432063
• 负责人: Ralph Greenspan
• 金额: $ 55万
• 依托单位: Neurosciences Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0432063&HistoricalAwards=false
• 关键词: CompBio; Gene; Interactions; Model; Network

The extensive interactivity among genes that is now being revealed suggests that there is considerable flexibility in the genome's capacity for responding effectively to diverse conditions. In a model gene network, centering on a temperature-sensitive mutation in the Syntaxin1A gene affecting synaptic transmission in Drosophila, a high degree of flexibility has been observed and the interactions underlying the various states of the network will now be analyzed. The phenotypic space of the network's functionality will be explored and the patterns of gene expression associated with various different genotypes and system outputs assayed. Functional clustering analysis, a measure based on the mutual information in the system, will be applied to these data to construct testable models and predictions of gene interaction. The outcome of this research offers possibilities for new kinds of network strategies and architectures. The overall goal of the work is to test the idea that gene networks operate by the same fundamental principles as neuronal networks, of which degeneracy is one of the key characteristics. The gene network surrounding Syntaxin will be analyzed by synthesizing various allele combinations, dividing them into groups with quantitatively similar phenotypes, and comparing the gene expression patterns within and between groups. A particular focus will be those genotypes that produce similar scores, as a window into the various network configurations that are capable of producing similar outputs (i.e., system degeneracy). From this analysis, models will be generated and predictions made of which (and how many) gene combinations stabilize the phenotype, and these will be tested by constructing and analyzing further mutant combinations. Aim 1: Perform bi-directional selection on a population of flies in which all of these alleles (Syx1A3-69and EPs) are randomly segregating to derive strains with extreme sensitivity or resistance to paralysis. Aim 2: Perform array analyses on a subset of phenotypic groups of genotypes from EP and Df analyses, as well as on the selected and control strains. Aim 3: Apply functional clustering based on phenotype and array results, and make predictions on phenotypes of novel combinations. Test novel combinations phenotypically, and molecularly. Gene networks are likely to share common organizational and operational principles with neuronal networks, and with biological networks in general, despite their very different modes and kinetics of internal communication and connection. This proposal addresses the issue directly, by taking a representative gene network and analyzing it with tools developed and validated for neuronal networks. The experiments outlined above constitute a new approach to the question of whether there are fundamental underlying principles of biological network operation which, if discerned, would have wide-ranging implications for the design and implementation of artifical networks constructed for applications as diverse as computing, engineered adaptive devices, and communications.

目前正在揭示的基因之间广泛的相互作用表明，基因组有效响应不同条件的能力具有相当大的灵活性。 在模型基因网络中，以影响果蝇突触传递的 Syntaxin1A 基因中的温度敏感突变为中心，观察到了高度的灵活性，现在将分析网络各种状态下的相互作用。 将探索网络功能的表型空间，并分析与各种不同基因型和系统输出相关的基因表达模式。 功能聚类分析是一种基于系统中互信息的测量，将应用于这些数据以构建可测试的模型和基因相互作用的预测。 这项研究的结果为新型网络策略和架构提供了可能性。这项工作的总体目标是检验基因网络与神经元网络的基本原理相同的想法，其中简并性是其关键特征之一。 将通过合成各种等位基因组合、将它们分成具有定量相似表型的组、并比较组内和组间的基因表达模式来分析突触蛋白周围的基因网络。 特别关注的是那些产生相似分数的基因型，作为了解能够产生相似输出（即系统简并性）的各种网络配置的窗口。 根据该分析，将生成模型并预测哪些（以及多少）基因组合稳定表型，并且将通过构建和分析进一步的突变体组合来测试这些模型。目标 1：对所有这些等位基因（Syx1A3-69 和 EP）随机分离的果蝇群体进行双向选择，以获得对麻痹具有极高敏感性或抗性的品系。 目标 2：对 EP 和 Df 分析中的基因型表型组的子集以及所选菌株和对照菌株进行阵列分析。目标 3：应用基于表型和阵列结果的功能聚类，并对新组合的表型进行预测。 从表型和分子角度测试新的组合。基因网络很可能与神经元网络以及一般的生物网络共享共同的组织和操作原则，尽管它们的内部通信和连接的模式和动力学截然不同。 该提案通过采用代表性基因网络并使用为神经元网络开发和验证的工具对其进行分析，直接解决了该问题。 上述实验为解决生物网络运行是否存在基本原理这一问题提供了一种新方法，如果能够认识到这些原理，将对为计算、工程自适应设备和通信等多种应用构建的人工网络的设计和实现产生广泛的影响。