Nested Event Representation for Automated Assembly of Cell Signaling Network Models

The rate at which biological literature is published far outpaces the current capabilities of modeling experts. In order to facilitate the automation of model assembly, we improve upon methods for converting machine reading output obtained from papers studying intracellular networks into discrete element rule-based models. We introduce a graph representation that can capture the complicated semantics found in machine reading output. Specifically, we focus on extracting change-of-rate information available when network elements are found to inhibit or catalyze other interactions (nested events). We demonstrate the viability of this approach by measuring the prevalence of these nested events in cancer literature, as well as the success rates of two machine readers in capturing them. Finally, we show how our algorithm can translate between machine reading output and the new graphical form. By incorporating these more detailed interactions into the model, we can more accurately predict cellular dynamics on a broad scale, leading to improvements in experimental design and disease treatment discovery.