This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes. It also discusses the inference of model parameters from experimental data and control strategies for driving network behavior towards desirable states.The PBN model is well suited to serve as a mathematical framework to study basic issues dealing with systems-based genomics, specifically, the relevant aspects of stochastic, nonlinear dynamical systems. The book builds a rigorous mathematical foundation for exploring these issues, which include long-run dynamical properties and how these correspond to therapeutic goals; the effect of complexity on model inference and the resulting consequences of model uncertainty; altering network dynamics via structural intervention, such as perturbing gene logic; optimal control of regulatory networks over time; limitations imposed on the ability to achieve optimal control owing to model complexity; and the effects of asynchronicity.The authors attempt to unify different strands of current research and address emerging issues such as constrained control, greedy control, and asynchronicity.
