Detection and Understanding of Expression Differentiatio

描述（由申请人提供）：最近的研究表明，从酵母到哺乳动物的许多真核生物中可能存在寿命调节。在这个项目中，我们计划获得具有正常和延长寿命的酵母菌株的时间过程中的表达谱，开发新的统计方法来检测表达差异，开发新的统计和计算方法来理解导致延长寿命的途径，并传播该项目产生的数据和软件。 从微阵列测量中，我们寻求不同生物样品之间mRNA表达的差异。归一化的统计处理旨在减少由于不受控制的变化而导致的军队特有的“块效应”。为了调整背景和尺度中的空间模式，我们提出了子阵列归一化。根据我们的实验设计，阵列之间可能存在实质性的差异，我们检测它们的最小修剪平方的技术，其精确的解决方案，可以通过我们最近开发的快速和稳定的算法计算。 从微阵列分析菌株，如sch 9？RAS2？突变体，那些具有显着分化的基因将作为未来研究的种子。我们将朝着几个方向努力。首先，我们寻找与种子共表达的基因。其次，我们寻找与种子相关的m-表达式的变化。第三，我们研究了与种子基因相关的调控。 第四，使用蛋白质-蛋白质相互作用数据库，我们试图确定蛋白质复合物以及调节酵母寿命的途径。 许多基因组或相互作用数据，如蛋白质-DNA相互作用数据，可以安排在一个二进制阵列。我们介绍了有向无环布尔（DAB）网络的结构作为一种工具，探索生物学途径的二进制阵列。有了一些合理的初始网络，我们将使用更复杂的贝叶斯网络来完善和改进最终结果。 本计画的目的是透过系统的酵母表达实验，找出延长寿命的原因。为了服务于科学使命，我们开发和整合统计和计算方法。我们的研究将有益于社会对老龄化的新认识。

DESCRIPTION (provided by applicant): Recent studies suggest that the regulation of longevity may be conserved in many eukaryotes ranging from yeast to mammals. In this project, we plan to obtain expression profiles on a time course for yeast strains with normal and extended life span, to develop novel statistical methods to detect expression differentiation, to develop new statistical and computational method to understand the pathways leading to extended life span, and to disseminate data and software resulting from the project. From microarray measurement, we seek differentiation of mRNA expressions among different biological samples. The statistical treatment of normalization aims to reduce army-specific "block effect" due to uncontrolled variation. To adjust for spatial patterns in both background and scale, we propose sub-array normalization. According to our experimental design, substantial differentiation may exist among arrays and we detect them by the technique of least trimmed squares, whose exact solution can be computed by a fast and stable algorithm we developed recently. From microarray analysis of strains such as sch9? and ras2? mutants, those genes with significant differentiation will serve as seeds for future investigation. We will pursue several directions. First, we search for genes that are co-expressed with seeds. Second, we search for changes of m-expressions associated with seeds. Third, we investigate regulation related to seed genes. Fourth, using the protein-protein interaction databases available for growing yeast we attempt to identify protein complexes as well as pathways that regulate longevity in yeast. Many genomic or interaction data such as protein-DNA interaction data can be arranged in a binary array. We introduce the structure of directed acyclic Boolean (DAB) networks as a tool of exploring biological pathways from binary arrays. With a few reasonable starting networks, we wilt use more sophisticated Bayesian networks to polish and refine the final results. This project aims to discover causations of life span extension from systematic experiments of yeast expression. To serve the scientific mission, we develop and integrate statistical and computational methodologies. Our research will benefit the society by new understanding of aging.