Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory NetworksThis 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." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science. |
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algorithm applied assume asynchronous attractor cycle attractor structure average basin of attraction Bayesian networks behavior binary Bioinformatics biological Boolean function Boolean networks cell complexity compute consider constituent BN constituent networks context context-sensitive PBNs control gene control input control policy corresponding cost function cost per stage CycD defined denote Dougherty estimate f₁ Faryabi finite-horizon function f function perturbation gene expression gene regulatory networks gene-activity profile genetic given inference instantaneously random PBNs long-run sensitivity Markov chain minimal network function nodes number of genes one-bit perturbation optimal control parameter perturbation probability predictor functions predictor set probabilistic Boolean networks problem procedure Q-learning randomly reduced BN respectively robust selection probabilities Shmulevich singleton attractor SMA-PBN stationary policy steady-state distribution steady-state probabilities step target gene Theorem transition diagram transition matrix truth table undesirable updating Vahedi vector WNT5A X₁