Title
Identification of highly synchronized subnetworks from gene expression data.
Abstract
There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.Inclusion of interaction dynamics is important to the identification of relevant gene networks.
Year
DOI
Venue
2013
10.1186/1471-2105-14-S9-S5
BMC Bioinformatics
Keywords
Field
DocType
gene regulatory networks,nonlinear dynamics,computer simulation,computational biology,gene expression profiling,microarrays,bioinformatics,cell cycle,algorithms
DNA binding site,Biology,Gene expression,Bioinformatics,Computational biology,Genetics,Gene regulatory network,Gene expression profiling,DNA microarray
Journal
Volume
Issue
ISSN
14 Suppl 9
S-9
1471-2105
Citations 
PageRank 
References 
8
0.38
11
Authors
2
Name
Order
Citations
PageRank
Shouguo Gao1502.65
Xujing Wang2908.54