CompBio: Gene Interactions as a Model for Network Architectures

目前正在揭示的基因之间广泛的相互作用表明，基因组对不同条件做出有效反应的能力有相当大的灵活性。在一个模型基因网络中，以影响果蝇突触传递的Synaxin1A基因的温度敏感突变为中心，观察到了高度的灵活性，现在将分析该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类型和系统输出相关的基因表达模式。基于系统中互信息的功能聚类分析将被应用于这些数据，以构建可检验的基因交互作用模型和预测。这项研究的结果为新型网络战略和架构提供了可能性。这项工作的总体目标是测试这样一种想法，即基因网络与神经网络遵循相同的基本原理，简并性是其关键特征之一。围绕Synaxin的基因网络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。一个特别的焦点将是那些产生相似分数的基因类型，作为了解能够产生相似输出(即系统退化)的各种网络配置的窗口。根据这一分析，将生成模型并预测哪些(以及多少)基因组合稳定了表型，并将通过构建和分析进一步的突变组合来测试这些模型。目的1：对所有这些等位基因(Syx1A3-69和EPs)随机分离的果蝇群体进行双向选择，以获得对麻痹极端敏感或抵抗的品系。目的2：对来自EP和DF分析的表型组的一个子集进行阵列分析，以及对选择的和对照的菌株进行分析。目的3：应用基于表型和阵列结果的函数聚类，对新组合的表型进行预测。从表型和分子上测试新的组合。基因网络可能与神经元网络和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非常不同。这一建议直接解决了这个问题，方法是选取一个有代表性的基因网络，并使用为神经元网络开发和验证的工具对其进行分析。上面概述的实验构成了一种新的方法，以解决是否存在生物网络运行的基本基本原则的问题，如果发现这些基本原则，将对为计算、工程自适应设备和通信等各种应用而构建的人工网络的设计和实施产生广泛的影响。

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.