COMPUTATIONAL ANALYSIS OF GENOME EXPRESSION DATA

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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.