A query-based technique for interpreting reachable sets for hybrid automaton models of protein feedback signaling

Intercellular protein signaling networks involving feedback have been modeled successfully using hybrid automata. Algorithms have also been developed to compute symbolic reachable sets for the biologically observed steady states of the models. However, the complexity and size of the reachable sets have made it difficult to interpret them. This paper presents a systematic and efficient algorithm for interpreting large reachable sets by treating them as a database, and using logical queries that encode biologically meaningful properties, to elucidate specific behavior of the modeled system. The results of several query computations for a four cell Delta-Notch protein signaling network model are also given, to illustrate the technique and its utility.