  With the tremendous increase in the amount of biological literature,
developing automated methods for extracting big data from papers, building
models and explaining big mechanisms becomes a necessity. We describe here our
approach to translating machine reading outputs, obtained by reading bio-
logical signaling literature, to discrete models of cellular networks. We use
out- puts from three different reading engines, and describe our approach to
translating their different features, using examples from reading cancer
literature. We also outline several issues that still arise when assembling
cellular network models from state-of-the-art reading engines. Finally, we
illustrate the details of our approach with a case study in pancreatic cancer.
