Probabilistic Boolean Networks – The Modeling and Control of Gene Regulatory Networks

The first comprehensive treatment of probabilistic Boolean networks (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. As the PBN model can serve as a mathematical framework for studying the basic issues of systems-based genomics, 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 unify different strands of current research and address emerging issues such as constrained control, greedy control, and asynchronicity.

Ilya Shmulevich is a Professor at the Institute for Systems Biology, Seattle, WA. Dr Shmulevich received his Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA, in 1997. He has since worked at the University of Nijmegen, the University of Amsterdam, Tampere University of Technology, the University of Texas and Rice University.

Edward R. Dougherty is a Professor and Director of the Genomic Signal Processing Laboratory at Texas A & M University, College Station, TX. He is also Co-Director of the Computational Biology Division of the Translational Genomics Research Institute, Phoenix, AZ.