Biological network motif detection: Principles and practice

Briefings in Bioinformatics

Volumn 13, Issue 2, 2012, Pages 202-215

  Wong, Elisabeth ^a   Baur, Brittany ^b   Quader, Saad ^c   Huang, Chun Hsi ^c  

^a BOWDOIN COLLEGE   (United States)

^b MANHATTANVILLE COLLEGE   (United States)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Biological networks; Graph isomorphism; Network motifs

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BIOLOGY; GENE REGULATORY NETWORK; METHODOLOGY; PROTEIN ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; MODELS, BIOLOGICAL; PROTEIN INTERACTION MAPPING;

EID: 84863253706     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr033     Document Type: Article

References (55)

1. Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science 2002; 298:824-27.
2. Shen-Orr S, Milo R, Mangan S, et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 2002;31:64-8.
3. Milo R, Kashtan N, Itzkovitz S, et al. On the uniform generation of random graphs with prescribed degree sequences. Arxiv preprint, cond-mat/0312028v2, 2004.
4. Shoval O, Alon U. SnapShot: Network Motifs. Cell 2010; 143:326.e1.
5. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet 2007;8:450-61.
6. Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 2003;100:11980-85.
7. Mangan S, Itzkovitz S, Zaslaver A, et al. The incoherent feed-forward loop accelerates the response time of the gal system of Escherichia coli. J Mol Biol 2006;356:1073-81.
8. Lee TI. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002;298:799-804.
9. Ratushny AV, Ramsey SA, Roda O, et al. Control of transcriptional variability by overlapping feed-forward regulatory motifs. Biophys J 2008;95:3715-23.
10. Kalir S, Mangan S, Alon U. A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli. Mol Syst Biol 2005;1:2005.0006.
11. Setty Y, Mayo AE, Surette MG, et al. Detailed map of a cis-regulatory input function. Proc Natl Acad Sci USA 2003; 100:7702-07.
12. Dobrin R, Beg QK, Barabási A-L, et al. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics 2004;5:10.
13. Kim J-R, Yoon Y, Cho K-H. Coupled feedback loops form dynamic motifs of cellular networks. Biophys J 2008; 94:359-65.
14. Kashtan N, Itzkovitz S, Milo R, et al. Topological generalizations of network motifs. Phys Rev E 2004;70:31909.
15. Milo R, Itzkovitz S, Kashtan N, et al. Superfamilies of evolved and designed networks. Science 2004;303:1538-42.
16. Itzkovitz S, Levitt R, Kashtan N, et al. Coarse-graining and self-dissimilarity of complex networks. Phys Rev E 2005;71: 016127.
17. Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proc Natl Acad Sci USA 2005;102: 13773-78.
18. Solé RV, Valverde S. Are network motifs the spandrels of cellular complexity? Trends Ecol Evol 2006;21:419-22.
19. Bottani S, Vergassola M, Mazurie A. An evolutionary and functional assessment of regulatory network motifs. Genome Biol 2005;6:R35.
20. Kashani ZRM, Ahrabian H, Elahi E, et al. Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 2009;10:318.
21. Grochow JA, Kellis M. Network motif discovery using sub-graph enumeration and symmetry-breaking. Res Comp Mol Biol 2007;4456:92-106.
22. Kuramochi M, Karypis G. Frequent sub-graph discovery. In: Proceedings of IEEE International Conference on Data Mining 2001, San Jose, California, 313-20.
23. Sloane NJA, Plouffe S. Number of graphs on n unlabeled nodes. (A000088, Formerly M1253 N0479).The Online Encyclopedia of IntegerSequences 2010. http://oeis.org (15 January 2011, date last accessed).
24. Han J, Kamber M. Data Mining: Concepts and Techniques. 2nd edn. Morgan Kaufmann (an imprint of Elsevier), 2006.
25. Ciriello G, Guerra C. A review on models and algorithms for motif discovery in protein-protein interaction networks. Brief Funct Genomics 2008;7:147-56.
26. Ribeiro P, Silva F, Kaiser M. Strategies for network motifs discovery. In: Proceedings of the 5th IEEE International Conference on e-Science 2009, Oxford, UK, 80-87.
27. Omidi S, Schreiber F, Masoudi-Nejad A. MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genetic Syst 2009;84:385-95.
28. Schreiber F, Schwöbbermeyer H. Frequency concepts and pattern detection for the analysis of motifs in networks. Lect Notes Comput Sci 2005;3737:89-104.
29. Kuramochi M, Karypis G. Finding frequent patterns in a large sparse graph. Data Min Knowl Disc 2005;11:243-71.
30. Fortin S. The Graph isomorphism problem. Technical report 1996, Department of Computing Science, University of Alberta, Edomonton, Alberta, Canada, 1996.
31. Garey M, Johnson D. Computers and Intractability: AGuide to theTheory of NP-Completeness. New York: W. H. Freeman and Co., 1979.
32. Babai L, Codenotti P. Isomorphism of hypergraphs of low rank in moderately exponential time. In: Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science 2008, Philadelphia, PA, USA, 667-76.
33. Johnson DS. The NP-completeness column. ACM Trans Algorithms 2005;1:160-76.
34. Cook SA. The complexity of theorem-proving procedures. In: Proceedings of the third Annual ACM Symposium onTheory of Computing 1971, Shaker Heights, OH, USA, 151-8.
35. Babai L, Luks EM. Canonical labeling of graphs. In: Proceedings of the Fifteenth Annual ACM symposium on Theory of Computing 1983, Boston, MA, USA, 171-83.
36. McKay B. The NAUTY Page. http://cs.anu.edu.au/~bdm/nauty/ (5 February 2011, date last accessed).
37. Ziv E, Koytcheff R, Middendorf M, et al. Systematic identification of statistically significant network measures. Phys Rev E 2005;71:016110.
38. Hastie T, Tibshirani R, Friedman J. TheElements ofStatistical Learning: Data Mining, Inference and Prediction. 2nd edn. Springer NYC, NY, USA: Springer Series in Statistics, 2009.
39. Kashtan N, Itzkovitz S, Milo R, et al. Efficient sampling algorithm for estimating sub-graph concentrations and detecting network motifs. Bioinformatics 2004;20:1746-58.
40. Wernicke S, Rasche F. FANMOD: A tool for fast network motif detection. Bioinformatics, 2006;22:1152-3.
41. Barabási A. Emergence of scaling in random networks. Science 1999;286:509-12.
42. Schreiber F, Schwöbbermeyer H. MAVisto: a tool for the exploration of network motifs. Bioinformatics 2005;21: 3572-4.
43. Chen J, Hsu W, Le ML, et al. NeMoFinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006, Philadelphia, PA, USA, 106-15.
44. Kashtan N, Itzkovitz S, Milo R, et al. Network motif detection tool Mfinder tool guide. Technical report 2005, Departments of Molecular Cell Biology and Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel, 2005.
45. Wernicke S. A faster algorithm for detecting network motifs. Algorithms Bioinformatics 2005;3692:165-77.
46. Vázquez A, Dobrin R, Sergi D, et al. The topological relationship between the large-scale attributes and local interaction patterns of complex networks. Proc Natl Acad Sci USA 2004;101:17940-45.
47. Harary F, Palmer EH. Graphical Enumeration. New York: Academic Press, 1973.
48. Inokuchi A, Washio T, Motoda H. An Apriori-based algorithm for mining frequent substructures from graph Data. Principles Data Mining Knowl Disc 2000;1910:13-23.
49. Kuramochi M, Karypis G. An efficient algorithm for discovering frequent sub-graphs. IEEE Tran Knowl Data Eng 2004;16:1038-51.
50. Ullmann JR. An algorithm for subgraph isomorphism. JACM 1976;23:31-42.
51. Huan J, Wang W, Prins J, et al. Spin: mining maximal frequent sub-graphs from graph databases. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery andDataMining 2004, Seattle, WA, USA, 581-6.
52. Kreher DL, Stinson DR. Combinatorial Algorithms: Generation, Enumeration and Search. Boca Raton, FA, USA: CRC Press, 1999;329.
53. McKay B. Isomorph-free exhaustive generation. J Algorithms 1998;26:306-24.
54. Uetz P, Giot L, Cagney G, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000;403:623-7.
55. Lepistö T, Salomaa A, Lingas A, et al. A polynomial-time algorithm for sub-graph isomorphism of two-connected series-parallel graphs. In: Proceedings of the 15th International Colloquium on Automata, Languages and Programming (ICALP) 1988, Tampere, Finland, 394-409.