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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth008
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 5, 2003
Accepted December 1, 2003

Article

External control in Markovian genetic regulatory networks: the imperfect information case

Aniruddha Datta 1*, Ashish Choudhary 1*, Michael L. Bittner 2*, Edward R. Dougherty 3*

1 Department of Electrical Engineering, Texas A & M University, College Station, TX 77843-3128, USA
2 TGEN, 400 North Fifth Street, Suite 1600, Phoenix, Arizona 85004, U. S. A.
3 Department of Electrical Engineering, Texas A & M University, College Station, TX 77843-3128, USA; Department of Pathology, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, U. S. A.

* To whom correspondence should be addressed. E-mail: edward{at}ee.tamu.edu.


  Abstract

Probabilistic Boolean Networks (PBN's), 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.


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