Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Algorithms for Inference, Analysis and Control of Boolean Networks
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
Controllability and observability of Boolean control networks
Automatica (Journal of IFAC)
Intervention in context-sensitive probabilistic Boolean networks revisited
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Bayesian robustness in the control of gene regulatory networks
IEEE Transactions on Signal Processing
Scalable approach for effective control of gene regulatory networks
Artificial Intelligence in Medicine
IEEE Transactions on Signal Processing
Stationary and structural control in gene regulatory networks: basic concepts
International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
Automated large-scale control of gene regulatory networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Polynomial-time algorithm for controllability test of a class of Boolean biological networks
EURASIP Journal on Bioinformatics and Systems Biology
Identification of Boolean control networks
Automatica (Journal of IFAC)
Brief paper: Controllability of probabilistic Boolean control networks
Automatica (Journal of IFAC)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Heretofore, intervention has focused on finite-horizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinite-horizon control for context-sensitive probabilistic Boolean networks (PBNs). It can also be applied to instantaneously random PBNs. The stationary policy obtained is independent of time and dependent on the current state. This paper concentrates on discounted problems with bounded cost per stage and on average-cost-per-stage problems. These formulations are used to generate stationary policies for a PBN constructed from melanoma gene-expression data. The results show that the stationary policies obtained by the two different formulations are capable of shifting the probability mass of the stationary distribution from undesirable states to desirable ones.