Identification of coordinated gene expression and regulatory sequences

In the last few years genome sequencing projects have provided us with enormous amounts of DNA sequence, and the rate of new sequence accumulation continues to grow rapidly. Other sources of sequence data, such as EST projects, have further increased the information about what each genome contains. Computational tools have been essential for extracting important biological information from those sequences. Together these approaches have allowed us to establish a fairly extensive parts list" for many species. Among the remaining challenges are understanding how the various parts interaccom-bine to form complexes, regulate each others synthesis and activity, and in general interact to perform all of the functions necessary for the organism. Methods developed in the last few years can help to decipher these interactions , and undoubtedly new methods will emerge that will provide even greater details. Computational approaches to organize and analyze those data are already playing essential roles, but there are many open problems to be addressed in the coming years. The focus of this session is on the analysis of mRNA expression data. The data itself may be obtained in various ways, including ESTs and expression array methods. These techniques allow one to capture information about the level of gene expression for all of the genes simultaneously, and to monitor how the expression changes in a variety of experiments. For example, one can determine how expression changes over time from some xed point of reference , how i t c hanges in response to environmental stimuli, and how it diiers depending on the genetic background of the cells being studied. The raw data from such experiments, which consists of the expression levels for each gene, can be enormous and also undecipherable without computational tools to extract and highlight the most signiicant results. Papers in this session fall into two categories. The rst are those to identify sets of genes with related expression patterns, which can be used to infer functional relationships. Theoretical aspects of the problem are described and examples are presented from several