Algorithms to investigate transcriptional networks

描述(由申请人提供)：基因组研究的一个广泛目标是更好地了解转录调控。我们建议通过计算方法补充目前为实现这一目标所做的实验工作。基因转录受转录因子网络的调控；为了完成它们的任务，转录因子与基因附近的特定DNA元件结合，彼此相互作用，并与聚合酶相互作用。转录控制的总体任务被分配给功能密切的转录模块或转录模块的较小组，以便每个模块对特定刺激或环境条件做出反应来调节转录。这种控制的划分提供了一种模块化机制来共同调节功能相关的基因组。转录因子之间的组合相互作用和促进这些相互作用的DNA元件激发了研究转录调控的算法方法。我们将(1)开发一种EM方法，从全基因组芯片实验中同时检测转录因子结合基序及其相互作用伙伴；我们将把它应用于全基因组酵母ChlP-芯片数据和大鼠全基因组CREB结合数据，(2)开发新的图论方法来检测转录模块，并与实验者合作，应用这些方法来检测在两个生物过程-长期记忆存储和心力衰竭-中驱动转录的模块，(3)开发新的Gibbs采样方法来检测多部分图中的密集子图作为识别模块的手段，并将其应用于检测驱动人类组织特异性的模块。

DESCRIPTION (provided by applicant): A broad goal in genome research is to better understand transcriptional regulation. We propose to complement current experimental efforts towards this goal through computational approaches. Gene transcription is regulated by a network of transcription factors (TF); to accomplish their task, TFs bind to specific DNA elements in the relative vicinity of the gene, interact with each other and with polymerase. The overall task of transcriptional control is divided among smaller groups of closely functioning TFs, or transcriptional modules, such that each module regulates transcription in response to specific stimuli or environmental condition. This division of control provides a modular mechanism to co-regulate groups of functionally related genes. The combinatorial interactions among TFs and the DNA elements that facilitate these interactions motivate the algorithmic approaches to study transcriptional regulation. We will (1) develop an EM approach to simultaneously detect transcription factor binding motifs and their interacting partners from genome-wide ChIP experiments; we will apply this to genome-wide yeast ChlP-chip data and to genome-wide CREB binding data in rat, (2) develop novel graph-theoretic approaches to detect transcriptional modules, and apply the methods to detect modules driving transcription in two biological processes - long term memory storage and heart failure - in collaboration with experimentalists, (3) develop novel Gibbs sampling approach to detect dense sub-graphs in a multi-partite graph as a means to identify modules, and apply this to detect modules driving tissue-specificity in human.