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，许多真核生物对寿命的调节可能是保守的。在本项目中，我们计划获得正常寿命和延长寿命酵母菌株的时间序列表达谱，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了解导致延长寿命的途径，并传播项目产生的数据和软件。

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.