Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Dynamic Programming and Stochastic Control
Dynamic Programming and Stochastic Control
Dynamic Programming
Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Comparison of gene regulatory networks via steady-state trajectories
EURASIP Journal on Bioinformatics and Systems Biology
Reduction mappings between probabilistic Boolean networks
EURASIP Journal on Applied Signal Processing
Genomic signal processing: the salient issues
EURASIP Journal on Applied Signal Processing
Simulation study in Probabilistic Boolean Network models for genetic regulatory networks
International Journal of Data Mining and Bioinformatics
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)
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
Generating probabilistic Boolean networks from a prescribed stationary distribution
Information Sciences: an International Journal
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
Planning interventions in biological networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Polynomial-time algorithm for controllability test of a class of Boolean biological networks
EURASIP Journal on Bioinformatics and Systems Biology
The Benefit of Decomposing POMDP for Control of Gene Regulatory Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
A control model for markovian genetic regulatory networks
Transactions on Computational Systems Biology V
International Journal of Data Mining and Bioinformatics
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Probabilistic Boolean Networks (PBN's) have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. Such networks, which form a subclass of Markovian Genetic Regulatory Networks, provide a convenient tool for studying interactions between different genes while allowing for uncertainty in the knowledge of these relationships. This paper deals with the issue of control in probabilistic Boolean networks. More precisely, given a general Markovian Genetic Regulatory Network whose state transition probabilities depend on an external (control) variable, the paper develops a procedure by which one can choose the sequence of control actions that minimize a given performance index over a finite number of steps. The procedure is based on the theory of controlled Markov chains and makes use of the classical technique of Dynamic Programming. The choice of the finite horizon performance index is motivated by cancer treatment applications where one would ideally like to intervene only over a finite time horizon, then suspend treatment and observe the effects over some additional time before deciding if further intervention is necessary. The undiscounted finite horizon cost minimization problem considered here is the simplest one to formulate and solve, and is selected mainly for clarity of exposition, although more complicated costs could be used, provided appropriate technical conditions are satisfied.