Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 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. Dougherty 1, 2 1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA 2 Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA Correspondence should be addressed to Babak Faryabi, bfariabi@tamu.edu Received 25 August 2008; Revised 17 November 2008; Accepted 16 January 2009 Recommended by Javier Garcia-Frias 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. 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. 1. Introduction In biology, there are numerous examples where the (in)activation of one gene or protein can lead to a certain cellular functional state or phenotype. For instance, in a stable cancer cell line, the reproductive cell cycle is repeated, and cancerous cells proliferate with time in the absence of intervention. One can use the p53 gene if the intervention goal is to push the cells into apoptosis, or programmed cell death, to arrest the cell cycle. The p53 gene is the most well-known tumor suppressor gene, encoding a protein that regulates the expression of several genes such as Bax and Fas/APO1, which function is to promote apoptosis [1, 2]. In cultured cells, extensive experimental results indicate that when p53 is activated, for example, in response to radiation, it leads to cell growth inhibition or cell death [3]. The p53 gene is also used in gene therapy, where the target gene (p53 in this case) is cloned into a viral vector. The modiﬁed virus serves as a vehicle to transport the p53 gene into tumor cells to generate intervention [4, 5]. As this and many other examples suggest, it is prudent to use gene regulatory models to design therapeutic interventions that expediently modify the cell’s dynamics via external signals. These system- based intervention methods can be useful in identifying potential drug targets and discovering treatments to disrupt or mitigate the aberrant gene functions contributing to the pathology of a disease. The main objective of intervention is to reduce the likelihood of encountering the undesirable gene-activity pro- ﬁles associated with aberrant cellular functions. Probabilistic Boolean networks (PBNs), a class of discrete-time discrete- space Markovian gene regulatory networks, have been used to derive such therapeutic strategies [6]. These classes of models, which allow the incorporation of uncertainty into