A fundamental issue facing modern biology is the development of methods and tools to convert genomics and function genomics data into mechanistic understanding. Genome sequences and computational methods have provided us with tools to identify and annotate genes and other functional sequences with varying degrees of accuracy. Annotated sequence information in turn enables tools such as microarrays, 2-D gels, protein mass spectrometry and yeast 2-hybrids to measure RNA and protein levels and physical interactions on genome-wide or near genome-wide scales. Genome information has also enable high-throughput genetic mapping technologies that allow one to rapidly trace a given phenotype or trait to specific genetic regions. However, in most cases there is still a major gap between the collection of large-scale genomic/genetic data and the inference of biological mechanism for a particular state, disease, or clinical outcome. This gap between large-scale data collection and biological understanding has become extremely apparent in the last several years during which an increasing number of microarray-based works have been published which made little contribution to new biological understanding. The focus of my research efforts is on the creation of tools to connect expression data to biological meaning and the application of these tools to understanding host-virus interactions and the host innate immune response.