Simulation study in Probabilistic Boolean Network models for genetic regulatory networks
International Journal of Data Mining and Bioinformatics
Controllability and observability of Boolean control networks
Automatica (Journal of IFAC)
Planning for gene regulatory network intervention
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
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
Identification of Boolean control networks
Automatica (Journal of IFAC)
A pattern-oriented specification of gene network inference processes
Computers in Biology and Medicine
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 3.85 |
Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.