Journal Article

Adaptive intervention in probabilistic boolean networks

Ritwik Layek, Aniruddha Datta, Ranadip Pal and Edward R. Dougherty

in Bioinformatics

Volume 25, issue 16, pages 2042-2048
Published in print August 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI:
Adaptive intervention in probabilistic boolean networks

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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.


Journal Article.  5963 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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