Genomic Reconstruction of Yeast Transcription Networks

描述(由申请人提供)：生物体将其基因组的很大一部分用于编码调控信息，该信息规定了不同基因应在何时何地开启或关闭。这些信息对于了解发育、组织特异性和细胞对环境的反应是必不可少的，对于了解疾病的分子基础也非常重要。编码在DNA中的一维调控程序由转录因子执行，转录因子对不同的条件做出反应，并联合调节基因的表达，从而形成复杂的调控网络。这项研究的目标是开发理论模型和计算算法来破译调控程序，并重建模式生物酿酒酵母的转录网络。具体目标如下：1)系统地绘制基因组中转录因子的DNA识别位点和靶基因；2)识别环境和遗传扰动下每个转录因子的激活或失活；3)系统分析多个转录因子的组合调控；4)与两个实验实验室合作解决转录调控和组合调控的具体问题；5)为生物界建立数据库和在线工具，以分析基因表达数据和转录调控。我们将开发整合几个酵母物种基因组序列信息和全基因组功能数据的方法，并将生物信息学分析与基因调控机制模型相结合。

DESCRIPTION (provided by applicant): Organisms devote a significant fraction of their genomes to encoding regulatory information which specifies when and where different genes should be turned on or off. Such information is essential for understanding development, tissue specificity, and cellular response to the environment and has great importance for understanding the molecular basis of disease. The one dimensional regulatory programs encoded in DNA are executed by transcription factors which respond to different conditions and regulate gene expression combinatorially, leading to complex regulatory networks. The goal of the proposed research is to develop theoretical models and computational algorithms to decipher the regulatory programs and to reconstruct the transcription networks in the model organism Saccharomyces cerevisiae. The following are the specific aims: 1) to systematically map the DNA recognition sites and target genes of transcription factors in the genome, 2) to identify environmental and genetic perturbations under which each transcription factor is activated or deactivated, 3) to systematically analyze combinatorial regulation by multiple transcription factors, 4) collaborate with two experimental labs to address specific problems of transcriptional regulation and combinatorial control, and 5) to build databases and online tools for the biological community to analyze gene expression data and transcriptional regulation. We will develop methods integrating information from the genome sequences for several yeast species and genome-wide functional data, and we will combine bioinformatics analysis with mechanistic models of gene regulation.