CompBio: Gene Interactions as a Model for Network Architectures

点击翻译按钮获取中文摘要

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.