Symbolic Representation and Inference of Regulatory Network Structures

Abstract

Recent results have demonstrated the usefulness of symbolic approaches for addressing various problems in systems biology. One of the fundamental challenges in systems biology is the extraction of integrated signaling-transcriptional networks from experimental data. In this chapter, we present a general logic-based framework, called Abductive Regulatory Network Inference (ARNI), where we formalize the network extraction problem as an abductive inference problem. A general logical model is provided that integrates prior knowledge on molecular interactions and other information for capturing signal-propagation principles and compatibility with experimental data. Solutions to our abductive inference problem define signed-directed networks that explain how genes are affected during the experiments. Using in-silico datasets provided by the dialogue for reverse engineering assessments and methods (DREAM)) consortium, we demonstrate the improved predictive power and complexity of our inferred network topologies compared with those generated by other non-symbolic inference approaches, showing the suitability of our approach for computing complete realistic networks. We also explore how the improved expressiveness together with the modularity and flexibility of the logic-based nature of our approach can support automated scientific discovery where the validity of hypothesized biological ideas can be examined and tested outside the laboratory.