EURASIP Journal on Bioinformatics and Systems Biology
Volume 2009 (2009), Article ID 360864, 13 pages
doi:10.1155/2009/360864
Research Article

Intervention in Context-Sensitive Probabilistic Boolean Networks Revisited

Babak Faryabi,1 Golnaz Vahedi,1 Jean-Francois Chamberland,1 Aniruddha Datta,1 and Edward R. Dougherty1,2

1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
2Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA

Received 25 August 2008; Revised 17 November 2008; Accepted 16 January 2009

Academic Editor: Javier Garcia-Frias

Copyright © 2009 Babak Faryabi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters.