Jun Liu, Department of Statistics Harvard University Computational Approaches to Gene Regulation Understanding how genes are regulated in various circumstances (e.g., heatshock, starvation, etc.) is a central problem in molecular biology. The adoption of large-scale biological data generation techniques such as the mRNA microarrays has enabled researchers to tackle the gene regulation problem in a global way. I will survey some computational and statistical strategies developed by our group on how to effectively use the gene upstream sequence information in junction with mRNA expression microarray data to dissect the gene regulatory network. I will describe in detail a study of RacA binding activities in Bacillus Subtilis, explaining how statistical approaches helped the biologists discover RacA's binding sites. I will describe a new dimension reduction technique that has been applied successfully to our gene regulation studies and show a cute theorem supporting the technique. Host: Xiaoming Huo & Jeff Wu