PMID- 24886608
OWN - NLM
STAT- MEDLINE
DCOM- 20140922
LR  - 20211021
IS  - 1752-0509 (Electronic)
IS  - 1752-0509 (Linking)
VI  - 8
DP  - 2014 May 21
TI  - Gene perturbation and intervention in context-sensitive stochastic Boolean 
      networks.
PG  - 60
LID - 10.1186/1752-0509-8-60 [doi]
AB  - BACKGROUND: 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) x (2n ∙ k) (or 2n x 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. RESULTS: The notion of stochastic Boolean 
      networks (SBNs) is extended for the general model of PBNs, i.e., CSPBNs. This 
      yields a novel structure of context-sensitive SBNs (CSSBNs) for modeling the 
      stochasticity in a GRN. A CSSBN enables an efficient simulation of a CSPBN with a 
      complexity of O(nLk2n) for computing the state transition matrix, where L is a 
      factor related to the required sequence length in CSSBN for achieving a desired 
      accuracy. A time-frame expanded CSSBN can further efficiently simulate the 
      stationary behavior of a CSPBN and allow for a tunable tradeoff between accuracy 
      and efficiency. The CSSBN approach is more efficient than an analytical method 
      and more accurate than an approximate analysis. CONCLUSIONS: Context-sensitive 
      stochastic Boolean networks (CSSBNs) are proposed as an efficient approach to 
      modeling the effects of gene perturbation and intervention in gene regulatory 
      networks. A CSSBN analysis provides biologically meaningful insights into the 
      oscillatory dynamics of the p53-Mdm2 network in a context-switching environment. 
      It is shown that random gene perturbation has a greater effect on the final 
      distribution of the steady state of a network compared to context switching 
      activities. The CSSBN approach can further predict the steady state distribution 
      of a glioma network under gene intervention. Ultimately, this will help drug 
      discovery and develop effective drug intervention strategies.
FAU - Zhu, Peican
AU  - Zhu P
FAU - Liang, Jinghang
AU  - Liang J
FAU - Han, Jie
AU  - Han J
AD  - Department of Electrical and Computer Engineering, University of Alberta, 
      Edmonton, AB, Canada. jhan8@ualberta.ca.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20140521
PL  - England
TA  - BMC Syst Biol
JT  - BMC systems biology
JID - 101301827
RN  - 0 (Tumor Suppressor Protein p53)
RN  - EC 2.3.2.27 (Proto-Oncogene Proteins c-mdm2)
SB  - IM
MH  - *Gene Regulatory Networks
MH  - Glioma/genetics/metabolism
MH  - *Models, Genetic
MH  - Probability
MH  - Proto-Oncogene Proteins c-mdm2/metabolism
MH  - Stochastic Processes
MH  - Systems Biology/*methods
MH  - Tumor Suppressor Protein p53/metabolism
PMC - PMC4062525
EDAT- 2014/06/03 06:00
MHDA- 2014/09/23 06:00
PMCR- 2014/05/21
CRDT- 2014/06/03 06:00
PHST- 2013/02/11 00:00 [received]
PHST- 2014/04/22 00:00 [accepted]
PHST- 2014/06/03 06:00 [entrez]
PHST- 2014/06/03 06:00 [pubmed]
PHST- 2014/09/23 06:00 [medline]
PHST- 2014/05/21 00:00 [pmc-release]
AID - 1752-0509-8-60 [pii]
AID - 10.1186/1752-0509-8-60 [doi]
PST - epublish
SO  - BMC Syst Biol. 2014 May 21;8:60. doi: 10.1186/1752-0509-8-60.