Skip to main content
SpringerLink
Log in

Motifs in Biological Networks

  • Chapter
  • First Online:
Recent Advances in Biological Network Analysis

Abstract

Biological networks provide great potential to understand how cells function. Motifs in biological networks, frequent topological patterns, represent key structures through which biological networks operate. Studying motifs answers important biological questions. Finding motifs in biological networks remains to be a computationally challenging task as the sizes of the motif and the underlying network grow. Several algorithms exist in the literature to solve this problem. This chapter discusses the biological significance of network motifs, motivation behind solving the motif detection problem and the key challenges of this problem. We discuss different formulations of motif detection problem based on several orthogonal perspectives that change the problem definition as well as solution significantly. The first perspective considers the number of input networks involved (i.e., one or more than one networks). The second perspective focuses on the labeling (i.e., labeled or unlabeled) of the nodes and edges of the input network. The third one considers different frequency definitions of counting motif instances (i.e., F1, F2, and F3) in a network. The fourth perspective describes whether the underlying network is directed or undirected. The last one considers motif detection under different types of network models (i.e., deterministic, probabilistic, or dynamic model). As a case study for each formulation, we briefly discuss important existing methods from the literature. Finally, we conclude with future research directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N.: Efficient graphlet counting for large networks. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 1–10. IEEE, Piscataway (2015)

    Google Scholar 

  2. Albert, I., Albert, R.: Conserved network motifs allow protein–protein interaction prediction. Bioinformatics 20(18), 3346–3352 (2004)

    Article  CAS  PubMed  Google Scholar 

  3. Alon, N., Dao, P., Hajirasouliha, I., Hormozdiari, F., Sahinalp, S.C.: Biomolecular network motif counting and discovery by color coding. Bioinformatics 24(13), i241–i249 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ay, F., Dinh, T.N., Thai, M.T., Kahveci, T.: Finding dynamic modules of biological regulatory networks. In: International Conference on BioInformatics and BioEngineering (BIBE), 2010 IEEE , pp. 136–143. IEEE, Piscataway (2010)

    Google Scholar 

  5. Ay, F., Kellis, M., Kahveci, T.: SubMAP: aligning metabolic pathways with subnetwork mappings. J. Comput. Biol. 18(3), 219–235 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ay, A., Gong, D., Kahveci, T.: Hierarchical decomposition of dynamically evolving regulatory networks. BMC Bioinf. 16(1), 1 (2015)

    Article  CAS  Google Scholar 

  7. Babai, L., Luks, E.M.: Canonical labeling of graphs. In: ACM Symposium on Theory of Computing, pp. 171–183 (1983)

    Google Scholar 

  8. Bachmaier, C., Brandes, U., Schreiber, F.: Biological networks. In: Handbook of Graph Drawing and Visualization, pp. 621–651. Chapman and Hall/CRC, Boco Raton (2014)

    Google Scholar 

  9. Bader, J.S., Chaudhuri, A., Rothberg, J.M., Chant, J.: Gaining confidence in high-throughput protein interaction networks. Nat. Biotechnol. 22(1), 78–85 (2004)

    Article  CAS  PubMed  Google Scholar 

  10. Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  PubMed  Google Scholar 

  11. Barabási, A.-L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004)

    Article  PubMed  Google Scholar 

  12. Charlebois, D.A., Balázsi, G., Kærn, M.: Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys. Rev. E 89(5), 052708 (2014)

    Article  Google Scholar 

  13. Chen, J., Hsu, W., Lee, M.L., Ng, S.-K.: NeMOfinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs. In: ACM SIGKDD, pp. 106–115 (2006)

    Google Scholar 

  14. Chen, J., Hsu, W., Lee, M.L., Ng, S.-K.: Labeling network motifs in protein interactomes for protein function prediction. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 546–555. IEEE, Piscataway (2007)

    Google Scholar 

  15. Cook, S.A.: The complexity of theorem-proving procedures. In: ACM Symposium on Theory of Computing, pp. 151–158. ACM, New York (1971)

    Google Scholar 

  16. Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans. Knowl. Data Eng. 17(8), 1036–1050 (2005)

    Article  Google Scholar 

  17. Dorogovtsev, S.N., Mendes, J.F.F., Samukhin, A.N.: Structure of growing networks with preferential linking. Phys. Rev. Lett. 85(21), 4633 (2000)

    Article  CAS  PubMed  Google Scholar 

  18. Erdős, P., Rényi, A.: On random graphs. Publ. Math. Debr. 6, 290–297 (1959)

    Google Scholar 

  19. Fortin, S.: The graph isomorphism problem. Technical report, Technical Report 96-20, University of Alberta, Edmonton, Alberta (1996)

    Google Scholar 

  20. Gabr, H., Kahveci, T.: Characterization of probabilistic signaling networks through signal propagation. In: Computational Advances in Bio and Medical Sciences, pp. 1–2. IEEE, Piscataway (2014)

    Google Scholar 

  21. Gabr, H., Dobra, A., Kahveci, T.: Estimating reachability in dense biological networks. In: ACM Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 86–95 (2015)

    Google Scholar 

  22. Garey, M.R., Johnson, D.S.: Computers and intractability: a guide to the theory of NP-completeness. In: Computers and Intractability, p. 340. Macmillan, New York (1979)

    Google Scholar 

  23. Grochow, J.A., Kellis, M.: Network motif discovery using subgraph enumeration and symmetry-breaking. In: Research in Computational Molecular Biology, pp. 92–106. Springer, Berlin (2007)

    Google Scholar 

  24. Holder, L.B., Cook, D.J., Djoko, S., et al.: Substructure discovery in the subdue system. In: KDD Workshop, pp. 169–180 (1994)

    Google Scholar 

  25. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.-L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)

    Article  CAS  PubMed  Google Scholar 

  26. Kashani, Z.R.M., Ahrabian, H., Elahi, E., Nowzari-Dalini, A., Ansari, E.S., Asadi, S., Mohammadi, S., Schreiber, F., Masoudi-Nejad, A.: Kavosh: a new algorithm for finding network motifs. BMC Bioinf. 10(1), 318 (2009)

    Article  Google Scholar 

  27. Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746–1758 (2004)

    Article  CAS  PubMed  Google Scholar 

  28. Krogan, N.J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A.P., et al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)

    Article  CAS  PubMed  Google Scholar 

  29. Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. IEEE Trans. Knowl. Data Eng. 16(9), 1038–1051 (2004)

    Article  Google Scholar 

  30. Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov. 11(3), 243–271 (2005)

    Article  Google Scholar 

  31. Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., et al.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)

    Article  CAS  PubMed  Google Scholar 

  32. Masoudi-Nejad, A., Schreiber, F., Kashani, Z.R.M.: Building blocks of biological networks: a review on major network motif discovery algorithms. IET Syst. Biol. 6(5), 164–174 (2012)

    Article  CAS  PubMed  Google Scholar 

  33. McKay, B.D.: Isomorph-free exhaustive generation. J. Algorithms 26(2), 306–324 (1998)

    Article  Google Scholar 

  34. Milenković, T., Lai, J., Pržulj, N.: GraphCrunch: a tool for large network analyses. BMC Bioinf. 9(1), 70 (2008)

    Article  Google Scholar 

  35. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  CAS  PubMed  Google Scholar 

  36. Mukherjee, K., Hasan, M.M., Boucher, C., Kahveci, T.: Counting motifs in dynamic networks. BMC Syst. Biol. 12(1), 6 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  37. Omidi, S., Schreiber, F., Masoudi-Nejad, A.: MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genet. Syst. 84(5), 385–395 (2009)

    Article  PubMed  Google Scholar 

  38. Przytycka, T.M.: An important connection between network motifs and parsimony models. In: Research in Computational Molecular Biology, pp. 321–335. Springer, Berlin (2006)

    Google Scholar 

  39. Redner, S.: How popular is your paper? An empirical study of the citation distribution. Eur. Phys. J. B Condens. Matter Complex Syst. 4(2), 131–134 (1998)

    Article  CAS  Google Scholar 

  40. Ribeiro, P., Silva, F.: G-tries: an efficient data structure for discovering network motifs. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1559–1566. ACM, New York (2010)

    Google Scholar 

  41. Sarkar, A., Ren, Y., Elhesha, R., Kahveci, T.: Counting independent motifs in probabilistic networks. In: Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, New York (2016)

    Google Scholar 

  42. Schreiber, F., Schwöbbermeyer, H.: Frequency concepts and pattern detection for the analysis of motifs in networks. In: Transactions on Computational Systems Biology III, pp. 89–104. Springer, Berlin (2005)

    Google Scholar 

  43. Schübeler, D., Scalzo, D., Kooperberg, C., van Steensel, B., Delrow, J., Groudine, M.: Genome-wide DNA replication profile for drosophila melanogaster: a link between transcription and replication timing. Nat. Genet. 32(3), 438–442 (2002)

    Article  PubMed  Google Scholar 

  44. Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31(1), 64–68 (2002)

    Article  CAS  PubMed  Google Scholar 

  45. Todor, A., Dobra, A., Kahveci, T.: Characterizing the topology of probabilistic biological networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(4), 970–983 (2013)

    Article  Google Scholar 

  46. Todor, A., Dobra, A., Kahveci, T.: Counting motifs in probabilistic biological networks. In: ACM Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 116–125 (2015)

    Google Scholar 

  47. Tran, N.H., Choi, K.P., Zhang, L.: Counting motifs in the human interactome. Nat. Commun. 4, 1 (2013)

    Article  CAS  Google Scholar 

  48. Vanetik, N., Gudes, E., Shimony, S.E.: Computing frequent graph patterns from semistructured data. In: ICDM, pp. 458–465. IEEE, Piscataway (2002)

    Google Scholar 

  49. Wang, P., Lü, J., Yu, X.: Identification of important nodes in directed biological networks: A network motif approach. PloS One 9(8), e106132 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wernicke, S.: Efficient detection of network motifs. IEEE/ACM Trans. Comput. Biol. Bioinf. (TCBB) 3(4), 347–359 (2006)

    Google Scholar 

  51. Wuchty, S., Stadler, P.F.: Centers of complex networks. J. Theor. Biol. 223(1), 45–53 (2003)

    Article  PubMed  Google Scholar 

  52. Wuchty, S., Oltvai, Z.N., Barabási, A.-L.: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat. Genet. 35(2), 176–179 (2003)

    Article  CAS  PubMed  Google Scholar 

  53. Yan, X., Zhou, X., Han, J.: Mining closed relational graphs with connectivity constraints. In: ACM SIGKDD, pp. 324–333 (2005)

    Google Scholar 

  54. Yanover, C., Singh, M., Zaslavsky, E.: M are better than one: an ensemble-based motif finder and its application to regulatory element prediction. Bioinformatics 25(7), 868–874 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  Google Scholar 

  56. Zhou, X., Kao, M.-C.J., Wong, W.H.: Transitive functional annotation by shortest-path analysis of gene expression data. Proc. Natl. Acad. Sci. 99(20), 12783–12788 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhu, X., Gerstein, M., Snyder, M.: Getting connected: analysis and principles of biological networks. Genes Dev. 21(9), 1010–1024 (2007)

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Florida, Gainesville, FL, USA

    Rasha Elhesha, Aisharjya Sarkar & Tamer Kahveci

Authors
  1. Rasha Elhesha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aisharjya Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tamer Kahveci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tamer Kahveci .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Byung-Jun Yoon

  2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Xiaoning Qian

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Elhesha, R., Sarkar, A., Kahveci, T. (2021). Motifs in Biological Networks. In: Yoon, BJ., Qian, X. (eds) Recent Advances in Biological Network Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-57173-3_5

Download citation

Publish with us

Policies and ethics

Search

Navigation