Network motif identification and structure detection with exponential random graph models
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 m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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International Academy of Ecology and Environmental Sciences
2014-12-01
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| Series: | Network Biology |
| Subjects: | |
| Online Access: | http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/network-motif-identification-and-structure-detection.pdf |
| _version_ | 1818751574759964672 |
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| author | Munni Begum Jay Bagga Ann Blakey, et al. |
| author_facet | Munni Begum Jay Bagga Ann Blakey, et al. |
| author_sort | Munni Begum |
| collection | DOAJ |
| description | 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* 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. |
| first_indexed | 2024-12-18T04:37:44Z |
| format | Article |
| id | doaj.art-74e3db010be04a65855992e8ea4cf554 |
| institution | Directory Open Access Journal |
| issn | 2220-8879 2220-8879 |
| language | English |
| last_indexed | 2024-12-18T04:37:44Z |
| publishDate | 2014-12-01 |
| publisher | International Academy of Ecology and Environmental Sciences |
| record_format | Article |
| series | Network Biology |
| spelling | doaj.art-74e3db010be04a65855992e8ea4cf5542022-12-21T21:20:48ZengInternational Academy of Ecology and Environmental SciencesNetwork Biology2220-88792220-88792014-12-0144155169Network motif identification and structure detection with exponential random graph modelsMunni Begum0Jay Bagga1Ann Blakey, et al.2Ball State University, Muncie, IN 47306, USABall State University, Muncie, IN 47306, USABall State University, Muncie, IN 47306, USALocal 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* 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.http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/network-motif-identification-and-structure-detection.pdfbiological networksnetwork motifstranscriptional regulatory networkgraphical modelsexponential random graph modelsMarkov Chain Monte Carlo algorithms |
| spellingShingle | Munni Begum Jay Bagga Ann Blakey, et al. Network motif identification and structure detection with exponential random graph models Network Biology biological networks network motifs transcriptional regulatory network graphical models exponential random graph models Markov Chain Monte Carlo algorithms |
| title | Network motif identification and structure detection with exponential random graph models |
| title_full | Network motif identification and structure detection with exponential random graph models |
| title_fullStr | Network motif identification and structure detection with exponential random graph models |
| title_full_unstemmed | Network motif identification and structure detection with exponential random graph models |
| title_short | Network motif identification and structure detection with exponential random graph models |
| title_sort | network motif identification and structure detection with exponential random graph models |
| topic | biological networks network motifs transcriptional regulatory network graphical models exponential random graph models Markov Chain Monte Carlo algorithms |
| url | http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/network-motif-identification-and-structure-detection.pdf |
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