Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Reduction mappings between probabilistic Boolean networks
EURASIP Journal on Applied Signal Processing
The impact of function perturbations in Boolean networks
Bioinformatics
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Genomic Signal Processing (Princeton Series in Applied Mathematics)
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
A control model for markovian genetic regulatory networks
Transactions on Computational Systems Biology V
Optimal infinite-horizon control for probabilistic Boolean networks
IEEE Transactions on Signal Processing - Part II
Robust Intervention in Probabilistic Boolean Networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Dynamics Preserving Size Reduction Mappings for Probabilistic Boolean Networks
IEEE Transactions on Signal Processing
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A major reason for constructing gene regulatory networks is to use them as models for determining therapeutic intervention strategies by deriving ways of altering their long-run dynamics in such a way as to reduce the likelihood of entering undesirable states. In general, two paradigms have been taken for gene network intervention: (1) stationary external control is based on optimally altering the status of a control gene (or genes) over time to drive network dynamics; and (2) structural intervention involves an optimal one-time change of the network structure (wiring) to beneficially alter the long-run behaviour of the network. These intervention approaches have mainly been developed within the context of the probabilistic Boolean network model for gene regulation. This article reviews both types of intervention and applies them to reducing the metastatic competence of cells via intervention in a melanoma-related network.