Modeling in vivo Protein-DNA Interactions from High-Throughput Data MP1/1

描述(由申请人提供)： 基因表达调控是任何细胞生命中最基本的过程，它主要由转录因子介导(在单基因水平上)，即与DMA结合的调节蛋白。据报道，体内的DMA靶标识别有时不同于基于体外的模型。 了解在细胞环境中控制特定DMA识别的机制将深刻地加深我们对转录因子功能机制的理解，并将在生物医学研究中产生重大影响。此外，很明显，需要开发新的基序寻找算法，专门用于高通量的蛋白质-DNA在体内的相互作用数据。 这项拟议工作的近期目标是开发有效分析体内高通量蛋白质-DNA关联数据(如芯片上的芯片)并识别生物学上重要的顺式调控元件的方法和工具。更遥远的目标是了解控制转录因子与其基因组DNA靶标相互作用的规则。拟议的活动最初旨在通过扩展和测试各种方法和战略来开发这样一个新的主题寻找软件。测试将基于人工的和“真实的”数据，并将评估各种方法的优缺点。最好的方法将被用来分析现有的和新的芯片上数据，并预测顺式调控基序，这些基序将随后用生化方法进行确认。样本转录因子将被用来研究特定顺式调控模块对基因表达的影响，目标是开发一种方法学，允许建立完整的基因调控计算模型。最后，将在我们将产生的工具和数据的基础上和周围开发一个数据库和网络界面，以便进行有效的数据传播、分析和挖掘。 为了实现这些目标，需要生物化学实验和计算机算法开发的结合。染色质免疫沉淀实验将与启动子微阵列杂交(ChlP-on-Chip)相结合，以确定TGFbetal诱导的原代肺细胞转录因子的可能靶点。将对数据进行统计分析，以推断转录因子结合的适当定量模型。公开的和新产生的基因表达数据也将被统计分析，以评估某些顺式调控模块在下游基因表达中的效果。

DESCRIPTION (provided by applicant): The control of gene expression is the most fundamental process in the life of any cell and it is primarily mediated (at the single gene level) by transcription factors, the DMA-binding regulatory proteins. It has been reported that the DMA target recognition in vivo sometimes differs from the in vitro-based models. Understanding the mechanisms that govern the specific DMA recognition in a cellular environment will profoundly augment our understanding of the mechanisms of transcription factor function and will also have a major impact in biomedical research. Furthermore, it becomes apparent that new motif finding algorithms need to be developed that specifically for high-throughput protein-DNA in vivo interaction data. The immediate goal of the proposed work is to develop the methodologies and tools to efficiently analyze high-throughput in vivo protein-DNA association data (like ChIP on chip) and identify the biologically important cis-regulatory elements. The more distant goal is to understand the rules that govern the interactions of transcription factors with their genomic DMA targets. The proposed activity aims, initially, to develop such a new motif finding software by expanding and testing various methods and strategies. Tests will be based on artificial and "real" data and the strengths and weaknesses of the various methods will be assessed. The best performing methods will be used to analyze existing and new ChIP on chip data, and predict the cis-regulatory motifs, which they will be subsequently confirmed with biochemical methods. Example transcription factors will be used to study the effect of particular cis-regulatory modules on gene expression with a goal of developing the methodology that will allow for complete computational models of gene regulation to be built. Finally, a database and web-interface will be developed on and around the tools and the data we will produce that ill allow for efficient data dissemination, analysis and mining. To accomplish these goals a combination of biochemical experimentation and computational algorithmic development is needed. Chromatin immunoprecipitation experiments will be coupled with promoter microarray hybridization (ChlP-on-chip) to identify possible targets for TGFbetal-induced transcription factors in primary lung cells. The data will be analyzed statistically to infer the appropriate quantitative models of the transcription factor binding. Publicly available and newly generated gene expression data will also be analyzed statistically to assess the effect of certain cis-regulatory modules in the expression of the downstream genes.