Temporal Logic Based Synthesis of Experimentally Constrained Interaction Networks

Abstract

Synthesis methods based on formal reasoning are a powerful way to automate the process of constructing computational models of gene regulatory networks (GRNs) and increase predictive power by considering a set of consistent models that are guaranteed to satisfy known experimental data. Previously, a formal reasoning based approach enabling the synthesis and analysis of biological networks formalized using Abstract Boolean Networks (ABNs) was developed, where the precise interactions and update rules are only partially known. System dynamics can be constrained with specifications of some required behaviors, thereby providing a characterization of the set of all networks capable of reproducing given experimental observations. The synthesis method is supported by a tool, the Reasoning Engine for Interaction Networks (RE:IN). Starting with the synthesis framework supported by RE:IN, we provide translations of experimental observations to temporal logic and semantics of Abstract Boolean Networks, enabling us to use off-the-shelf model checking tools and algorithms. An initial prototype implementation we have developed demonstrates this is a gainful approach, providing speed-up gains for some benchmarks, while also opening the way to study extensions of the experimental observations specification language currently supported in RE:IN by using the rich expressive power of temporal logic.