Title |
Gene perturbation and intervention in context-sensitive stochastic Boolean networks
|
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Published in |
BMC Systems Biology, May 2014
|
DOI | 10.1186/1752-0509-8-60 |
Pubmed ID | |
Authors |
Peican Zhu, Jinghang Liang, Jie Han |
Abstract |
In a gene regulatory network (GRN), gene expressions are affected by noise, and stochastic fluctuations exist in the interactions among genes. These stochastic interactions are context dependent, thus it becomes important to consider noise in a context-sensitive manner in a network model. As a logical model, context-sensitive probabilistic Boolean networks (CSPBNs) account for molecular and genetic noise in the temporal context of gene functions. In a CSPBN with n genes and k contexts, however, a computational complexity of O(nk222n) (or O(nk2n)) is required for an accurate (or approximate) computation of the state transition matrix (STM) of the size (2n ∙ k) × (2n ∙ k) (or 2n × 2n). The evaluation of a steady state distribution (SSD) is more challenging. Recently, stochastic Boolean networks (SBNs) have been proposed as an efficient implementation of an instantaneous PBN. |
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