Network motif identification and structure detection with exponential
random graph models
Munni Begum
Jay Bagga
Ann Blakey
Sudipta Saha
Ball State University, Muncie, IN 47306, USA
University of South Carolina, Columbia, SC, USA
Network Biology
ISSN 2220-8879
http://www.iaees.org/publications/journals/nb/online-version.asp
2014
4
4
155
169
International Academy of Ecology and Environmental Sciences
Hong Kong
22 August 2014
25 September 2014
1 December 2014
biological networks
network motifs
transcriptional regulatory network
graphical models
exponential random graph models
Markov Chain Monte Carlo algorithms
Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli) through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p-star models, also known as Exponential Random Graph Models (ERGMs), to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC) computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network) and k-istar (or incoming star structures), for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models.
DOI 10.0000/issn-2220-8879-networkbiology-2014-v4-0013
http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/network-motif-identification-and-structure-detection.pdf