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Báo cáo hóa học: " Research Article Intervention in Context-Sensitive Probabilistic Boolean Networks Revisited'

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Intervention in Context-Sensitive Probabilistic Boolean Networks Revisited | 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. Dougherty1 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 .