Boolean modeling of biological regulatory networks: A methodology tutorial

Schematic representation of the basic steps of Boolean modeling of biological regulatory networks. [Display omitted] ► The basic steps of Boolean modeling of regulatory networks are presented. ► Inference of a Boolean network model from the available experimental data is described. ► Structural analysis of the inferred network using graph measures is explained. ► Different Boolean dynamic approaches are described. ► The obstacles one may encounter in the above steps are discussed. Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.