Robustness and Genetic Variation in Gene Networks

描述（由申请人提供）：用于研究大量基因和蛋白质的工具的最新发展导致了对确定基因网络的结构和功能的新兴趣，并且首次允许直接阐明基因网络的连接和逻辑组织。我们的理解的过程结构基因网络和确定这些网络的可变性一直受到限制，缺乏明确考虑网络结构的理论工作。基因网络很重要，因为它们包含产生复杂表型的相互作用，并且这些网络中基因的扰动可能导致其携带者的疾病或病原体的毒力增加。最近的理论工作试图对基因和蛋白质网络进行分类，将网络拓扑结构与进化速率相关联，并将网络拓扑结构与表型缺陷的大小相关联。这些研究没有提供网络功能进化的因果模型，也没有考虑特定基因网络的特征和约束。该建议的第一个目的是对基因网络的两个关键特征进行建模：对突变变化的鲁棒性和遗传方差的积累。这些相关的特征对于确定网络中哪些基因最有可能发生变化并与异常表型相关非常重要。第二个目标是将这些模型应用于细菌趋化性网络的特定情况。这个网络已经得到了很好的表征，DNA数据库已经包含了测试目标1所做预测的信息。第三个目标是根据目标2的结果改进趋化性网络模型，并开发使用比较基因组数据改进基因网络机制模型的一般方法。这项工作将增加我们对构建基因网络的力量的理解，以及我们预测哪些基因最有可能导致疾病的能力。

DESCRIPTION (provided by applicant): The recent development of tools for studying large numbers of genes and proteins has led to a new interest in determining the structure and function of gene networks and has, for the first time, allowed a direct elucidation of the connections and logical organization of gene networks. Our understanding of the processes which structure genes into networks and determine variability of those networks has been limited by a lack of theoretical work which explicitly takes network structure into account. Gene networks are important because they contain the interactions which produce complex phenotypes, and perturbations of the genes in these networks may cause disorders in their bearers or increased virulence in pathogens. Recent theoretical work has attempted to classify gene and protein networks, associate network topology with evolutionary rates, and associate network topology with the magnitude of phenotypic defects. These studies do not provide causal models of the evolution of network function, nor do they take into account features and constraints of particular gene networks. The first aim of this proposal is to model two key features of gene networks: Robustness to mutational change and accumulation of genetic variance. These related features are important in determining which genes in a network will be most likely to vary and to be associated with deviant phenotypes. The second aim is to apply these models to the specific case of the chemotaxis network of bacteria. This network is well characterized and DNA databases already contain the information to test the predictions made from aim 1. The third aim is to refine models of the chemotaxis network based on the outcome of aim 2, and to develop general methods for using comparative genomic data to refine mechanistic models of gene networks. This work will increase our understanding of the forces that structure gene networks and our ability to predict which genes are most likely to cause disease.