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Bioinformatics Advance Access originally published online on June 8, 2009
Bioinformatics 2009 25(16):2042-2048; doi:10.1093/bioinformatics/btp349
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Adaptive intervention in probabilistic boolean networks

Ritwik Layek 1, Aniruddha Datta 1, Ranadip Pal 2 and Edward R. Dougherty 1,3,*

1 Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX 77843, 2 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79410 and 3 Translational Genomics Research Institute, Phoenix, AZ 85004, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of time-course data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate full-model inference.

Results: This article demonstrates the feasibility of applying on-line adaptive control to improve intervention performance in genetic regulatory networks modeled by PBNs. It shows via simulations that when the network is modeled by a member of a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost. Two algorithms are presented, one better suited for instantaneously random PBNs and the other better suited for context-sensitive PBNs with low switching probability between the constituent BNs.

Contact: edward{at}ece.tamu.edu

Associate Editor: John Quackenbush

Received on March 9, 2009; revised on May 15, 2009; accepted on May 31, 2009

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