While the ever-increasing amounts of scientific data provide a more detailed description of toxic effects, it is not trivial to extract information that will contribute to a better biological understanding. Sophisticated computational methods have been developed to separate mathematically the biological signal from the noise in high-throughput datasets; however, visualizing and putting a signal into a relevant biological context using a priori knowledge is equally important. The Causal Biological Network (CBN) Database addresses these pressing needs in pulmonary and vascular biology. Each of the network models deposited in the CBN Database is scripted in the Biological Expression Language (BEL), a semantic language that represents scientific findings in a computable format. The biological areas covered by the CBN models include cell fate, response to cell stress, cell proliferation, inflammation, tissue repair, and angiogenesis, all in the context of the pulmonary and vascular systems. With specific biological boundaries, multiple types of gene-centric entities, literature support at edge level, and interactive visualization, the CBN Database offers a coherent illustration of important biological processes. Moreover, the computability of the CBN models provides the possibility of data-driven enhancement that delivers an efficient combination of literature knowledge and high-throughput data in a single model. The CBN Database can be applied in computational toxicology and could be extended to drug discovery, biomarker identification, and personalized medicine.