- Authors:
- Ilya Shmulevich,
- Edward R. Dougherty
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. Audience: 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. Contents: Preface; Chapter 1: Boolean Networks; Chapter 2; Structure and Dynamics of Probabilistic Boolean Networks; Chapter 3: Inference of Model Structure; Chapter 4: Structural Intervention; Chapter 5: External Control; Chapter 6: Asynchronous Networks; Bibliography; Index
Cited By
- Bahadorinejad A, Imani M and Braga-Neto U (2020). Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17:4, (1105-1114), Online publication date: 1-Jul-2020.
- Imani M and Braga-Neto U (2019). Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 16:4, (1250-1261), Online publication date: 1-Jul-2019.
- Mizera A, Pang J and Yuan Q (2019). GPU-accelerated steady-state computation of large probabilistic Boolean networks, Formal Aspects of Computing, 31:1, (27-46), Online publication date: 1-Feb-2019.
- Mizera A, Pang J, Qu H and Yuan Q (2019). Taming Asynchrony for Attractor Detection in Large Boolean Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 16:1, (31-42), Online publication date: 1-Jan-2019.
- Paul S, Su C, Pang J and Mizera A A Decomposition-based Approach towards the Control of Boolean Networks Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, (11-20)
- Zhou H, Khatri S, Hu J, Liu F and Sze C Fast and Highly Scalable Bayesian MDP on a GPU Platform Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, (158-167)
- Raza K and Alam M (2016). Recurrent neural network based hybrid model for reconstructing gene regulatory network, Computational Biology and Chemistry, 64:C, (322-334), Online publication date: 1-Oct-2016.
- Mizera A, Pang J and Yuan Q Parallel approximate steady-state analysis of large probabilistic Boolean networks Proceedings of the 31st Annual ACM Symposium on Applied Computing, (1-8)
- Qu H, Yuan Q, Pang J and Mizera A Improving BDD-based Attractor Detection for Synchronous Boolean Networks Proceedings of the 7th Asia-Pacific Symposium on Internetware, (212-220)
- Tran Q Selection of genes using long-term influence and sensitivity analysis Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, (658-664)
- Abdelzaher A, Mayo M, Perkins E and Ghosh P Correlating in silico feed-forward loop knockout experiments with the topological features of transcriptional regulatory networks Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies, (207-214)
- Pal R (2013). Modeling and inference of genetic interactions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3:6, (453-466), Online publication date: 1-Nov-2013.
- Lin P and Khatri S Application of logic synthesis to the understanding and cure of genetic diseases Proceedings of the 49th Annual Design Automation Conference, (734-740)
- Qian X and Dougherty E (2012). Intervention in Gene Regulatory Networks via Phenotypically Constrained Control Policies Based on Long-Run Behavior, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 9:1, (123-136), Online publication date: 1-Jan-2012.
- Roli A, Manfroni M, Pinciroli C and Birattari M On the design of Boolean network robots Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I, (43-52)
- Benedettini S, Roli A, Serra R and Villani M Stochastic local search to automatically design Boolean networks with maximally distant attractors Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I, (22-31)
Index Terms
- Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks
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