Symbolic Representation and Inference of Regulatory Network Structures

Abstract

This chapter presents a general logic-based framework, called Abductive Regulatory Network Inference (ARNI), where it 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 the 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, the chapter demonstrates the improved predictive power and complexity of the inferred network topologies compared with those generated by other non-symbolic inference approaches. It also explores how the improved expressiveness together with the modularity and flexibility of the logic-based nature can support automated scientific discovery where the validity of hypothesized biological ideas can be examined and tested outside the laboratory. © ISTE Ltd 2014. All rights reserved.