Detection and Understanding of Expression Differentiatio

描述(申请人提供)：最近的研究表明，在从酵母到哺乳动物的许多真核生物中，对寿命的调节可能是保守的。在这个项目中，我们计划获得正常和延长寿命的酵母菌株在一段时间内的表达谱，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了解导致寿命延长的途径，并传播该项目产生的数据和软件。 通过基因芯片检测，我们寻求不同生物样本之间的差异表达。常态化的统计处理旨在减少因不受控制的变异而产生的部队特有的“阻断效应”。为了适应背景和尺度下的空间模式，我们提出了子阵归一化方法。根据我们的实验设计，阵列之间可能存在很大的差异，我们使用最小剪裁平方技术来检测它们，其精确解可以通过我们最近开发的快速稳定的算法来计算。从基因芯片分析菌株，如sch9？那Ras2呢？突变体，那些分化明显的基因将作为未来研究的种子。我们将追求几个方向。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，使用可用于种植酵母的蛋白质-蛋白质相互作用数据库，我们试图识别蛋白质复合体以及调节酵母寿命的途径。 许多基因组或相互作用数据，例如蛋白质-DNA相互作用数据，可以排列成二进制阵列。我们介绍了有向无环布尔(DAB)网络的结构，作为从二进制阵列探索生物路径的工具。有了几个合理的开始网络，我们将使用更复杂的贝叶斯网络来润色和完善最终结果。 本项目旨在从酵母表达的系统实验中找出延长寿命的原因。为了服务于科学使命，我们开发并整合了统计和计算方法。我们的研究将通过对老龄化的新认识来造福社会。

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.