Dynamic information handling in continuous time Boolean Network model of gene interactions

Growing information and knowledge on gene regulatory networks, which are typical hybrid systems, has led a significant interest in modeling those networks. An important direction of gene network modeling is studying the abstract network models to understand the behavior of a class of systems. Boolean Networks has emerged as an important model class on this direction. Limitations of traditional Boolean Networks led the researchers to propose several generalizations. In this work, one such class, the Continuous Time Boolean Networks (CTBN’s), is studied. CTBN’s are constructed by allowing the Boolean variables evolve in continuous time and involve a biologically-motivated refractory period. In particular, we analyze the basic circuits and subsystems of the class of CTBN’s. We demonstrate the existence of various qualitative dynamic behavior including stable, multistable, neutrally stable, quasiperiodic and chaotic behaviors. We show that those models are capable of demonstrating highly adjustable features like maintenance of continuous protein concentrations. Finally, we discuss the relation between qualitative dynamic features and information handling.