This demonstration shows how systems biology can bring a more robust approach to understanding host-pathogen interactions. A computational systems biology method was employed to create an in silico interactome model of the bovine host responses to S. enterica Typhimurium (STM). To demonstrate this approach a bovine ligated ileal loop biological model was employed to capture the host gene expression response at multiple time points post infection concurrently with the pathogen gene expressions. Computational methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct pathogenicity analysis and to create the interactome model. For the bovine host, 219 signaling and metabolic pathways and 1620 Gene Ontology (GO) categories were profiled for perturbation against the control (uninfected condition) which defined the host’s biosignatures to the STM response. For the pathogen, 119 signaling and metabolic pathways and 781 GO categories were similarly profiled. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the host and pathogen response and used as building blocks for constructing the system-level interactome model. New algorithms for predicting host-pathogen protein-protein interactions (PPIs) from prior biological knowledge combined with host-pathogen gene expressions identified plausible host-pathogen interaction mechanisms that produced an interactome network. The interactome model becomes a tool that can be visualized, interrogated, and employed in simulation to help improve pathogenicity understanding and guide vaccine development and/or immunotherapeutic drug candidate selections.