Detection of exposure to biological threat agents has relied on ever more sensitive methods for pathogen identification, but that usually requires pathogen proliferation to dangerous, near untreatable levels. Recent events have demonstrated that assessing exposure to a biological threat agent well in advance of onset of illness or at various stages post-exposure is invaluable among the diagnostic options. There is an urgent need for better diagnostic tools that will be sensitive, rapid, and unambiguous. Since human clinical cases of illness induced by biothreat agents are, fortunately, rare, use of animal models that closely mimic the human illness is the only in vivo option. Such studies can be very difficult and expensive; therefore, maximizing the information obtained from in vitro exposures to peripheral blood mononuclear cells (PBMCs) provide an opportunity to investigate dose/time variability in host responses. In our quest to study staphylococcal enterotoxin B (SEB) induced host gene expression patterns, we addressed two core issues using microarray analysis and predictive modeling. Our first objective was to determine gene expression patterns in human PBMCs exposed to SEB in vitro. Second, we compared the in vitro data with host responses gene expression patterns in vivo using PBMCs from an animal model of SEB intoxication that closely replicates the progression of illness in humans. We used cDNA microarrays to study global gene expression patterns in piglets intoxicated with SEB. We applied a supervised learning method for class prediction based on the k-nearest neighbor algorithm for the data obtained in piglets exposed to SEB in vivo against a training data set. This data set included gene expression profiles derived from in vitro exposures to eight different pathogens (Bacillus anthracis, Yersinia pestis, Brucella melitensis, SEB, cholera toxin, Clostridium botulinum toxin A, Venezuelan equine encephalitis, and Dengue-2) in PBMCs. We found that despite differences in gene expression profiles between in vitro and in vivo systems, there exists a subset of genes that show correlations between in vitro and in vivo exposures, which can be used as a predictor of exposure to SEB in vivo.