After the successful sequencing of the genome of various organisms, the next step towards a larger goal of understanding the organization and function of a biological organism at a molecular level is to determine the spatial location where various genes are being expressed. Using a method known as high-throughput in situ hybridization, large numbers of 2D cross-sectional images of the mouse brain revealing gene expression patterns are being generated at Dr. Eichele's lab at Baylor College of Medicine. Yet such an enormous amount of data is fundamentally crippled without accompanying technology to convert the data into a form that allows scientists to rapidly query, compare, and analyze the expression patterns.

In this talk, we will describe our work in building a spatial database that allows biologists to organize and search this gene expression data for the mouse brain. The central component of this database is an atlas, represented as a set of meshes, that explicitly partitions the mouse brain into key anatomical regions. New gene expression images are added to the database by deforming this atlas onto each image and extracting the expression data from the image onto the atlas. We have successfully constructed a 2D version of the spatial database and made it available online at Geneatlas.org, and our objective is to construct a 3D gene expression database that allows fully 3D queries and analysis of spatial gene expression patterns for a significant portion of the mouse genome. The talk will give a brief review of our 2D gene expression database while focusing on some of the core computational problems that are crucial to the constructing of such a spatial database, such as building a deformable atlas of the mouse brain and registering this atlas onto gene expression images.