COMPUTATIONAL ANALYSIS OF GENOME EXPRESSION DATA

该项目的主要目标是开发和应用大规模基因表达数据分析和解释的计算方法。使用高密度DNA阵列来监测全基因组范围内的基因表达是生物学的一个根本进步。特别是，可以使用cDNA文库的序列微阵列杂交来查询生物体整个基因组中所有基因的动态表达模式。众所周知，复杂的基因表达模式是基因调控回路中基因动态相互作用网络的结果。调控DNA序列元件的层次化和模块化组织对基因表达的组合控制和调控具有重要意义。为了应对目前由于全基因组序列和DNA芯片的可用性而产生的大量基因表达数据的解释挑战，我们建议开发计算方法并在此类大规模实验数据的实际分析中进行测试。该提案涉及两个具体目标：即，(1)开发启动子数据库和灵活的计算工具，可以有效地促进转录组分析；(2)通过与领先的实验室科学家合作，研究特定生物系统和/或过程中调控顺式元件和转录调控机制，解决实际的生物学问题。我们认为这两个目标是不可分割的，没有计算生物学家和实验生物学家的紧密合作，我们解决复杂生物学问题的能力将受到严重限制。这一建议可能有助于我们理解两个相关的电路：一个是基因启动子结构中硬编码的遗传程序，另一个是通过相互作用的基因转录物和产物在时空中连接的网络，两者都可以在响应特定信号时被激活或表现出来。反过来，这些结果将有助于我们了解正常的生物过程，如细胞周期、体内平衡、生长发育以及病理表现，如代谢疾病、发育异常和癌症。

The primary objective of this project is the development and the application of computational methods for analysis and interpretation of large-scale gene expression data. The use of high-density DNA arrays to monitor gene expression at a genome- wide scale constitutes a fundamental advance in biology. In particular, the dynamic expression patterns of all genes in the entire genome of an organism can be interrogated using sequential microarray hybridization of cDNA libraries. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements is important for combinatorial control and regulation of gene expression. In order to meet the challenge of interpretation of massive gene expression data that are currently being generated as a result of the availability of complete genome sequences and DNA chips, we propose to develop computational methods and to test them in actual analyses of such large-scale experimental data. This proposal is concerned with two specific aims: namely, (1) developing promoter databases and flexible computational tools that can efficiently facilitate transcriptome analysis; (2) solving real biological problems by collaborating with leading bench-scientists on studies of regulatory cis-elements and the mechanism of transcriptional regulation in specific biological systems and/or processes. We believe these two aims are inseparable, without closely joining forces between computational and experimental biologists, our ability of attacking complex biological problems will be severely limited. This proposal is likely to contribute to our understanding of the two related circuitry: one that is hard coded as a genetic program in the gene promoter architecture and the other that is wired in space-time as a linked network by interacting gene transcripts and products, both can be activated or manifested in response to specific signals. In turn, the results will help us to understand normal biological processes such as cell cycle, homeostasis, growth and development as well as pathological manifestations such as metabolic diseases, developmental abnormalities and cancer.