Open AccessState reduction for network intervention in probabilistic Boolean networks
Author(s) -
Xiaoning Qian,
Noushin Ghaffari,
Ivan Ivanov,
Edward R. Dougherty
Publication year - 2010
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btq575
Subject(s) - markov chain , computer science , state space , probabilistic logic , reduction (mathematics) , boolean network , attractor , markov model , stationary distribution , continuous time markov chain , biological network , dynamic bayesian network , gene regulatory network , computational complexity theory , bayesian network , theoretical computer science , algorithm , mathematics , artificial intelligence , variable order markov model , machine learning , boolean function , statistics , mathematical analysis , geometry , combinatorics , biochemistry , gene expression , chemistry , gene
A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their long-run behavior using Markov chain theory. The long-run dynamics of a PBN, as represented by its steady-state distribution (SSD), can guide the design of effective intervention strategies for the modeled systems. A major obstacle for its application is the large state space of the underlying Markov chain, which poses a serious computational challenge. Hence, it is critical to reduce the model complexity of PBNs for practical applications.
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