Recipes for translating big data machine reading to executable cellular signaling models

Abstract

Biological literature is rich in mechanistic information that can be utilized to construct executable models of complex systems to increase our understanding of health and disease. However, the literature is vast and fragmented, and therefore, automation of information extraction from papers and of model assembly from the extracted information is necessary. We describe here our approach for translating machine reading outputs, obtained by reading biological signaling literature, to discrete models of cellular networks. We use outputs from three different reading engines, and demonstrate the translation of different features using examples from 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.