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Highly Bi-Connected Subgraphs for Computational Protein Function Annotation

  • Jucheol Moon
  • Iddo Friedberg
  • Oliver Eulenstein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9797)

Abstract

Identifying highly connected subgraphs in biological networks has become a powerful tool in computational biology. By definition a highly connected graph with n vertices can only be disconnected by removing more than \(\frac{n}{2}\) of its edges. This definition, however, is not suitable for bipartite graphs, which have various applications in biology, since such graphs cannot contain highly connected subgraphs. Here, we introduce a natural modification of highly connected graphs for bipartite graphs, and prove that the problem of finding such subgraphs with the maximum number of vertices in bipartite graphs is NP-hard. To address this problem, we provide an integer linear programming solution, as well as a local search heuristic. Finally, we demonstrate the applicability of our heuristic to predict protein function by identifying highly connected subgraphs in bipartite networks that connect proteins with their experimentally established functionality.

Keywords

Highly bi-connected Highly connected Bipartite graph Computational protein function annotation 

Notes

Acknowledgments

IF and OE acknowledge support from the National Science Foundation award # DBI 1458359 and #GS 133814.

References

  1. 1.
    Andreopoulos, B., An, A., Wang, X., Faloutsos, M., Schroeder, M.: Clustering by common friends finds locally significant proteins mediating modules. Bioinformatics 23(9), 1124–1131 (2007)CrossRefGoogle Scholar
  2. 2.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Dongbo, B., Zhao, Y., Cai, L., Xue, H., Zhu, X., Hongchao, L., Zhang, J., et al.: Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Res. 31(9), 2443–2450 (2003)CrossRefGoogle Scholar
  4. 4.
    Chang, W.-C., Vakati, S., Krause, R., Eulenstein, O.: Exploring biological interaction networks with tailored weighted quasi-bicliques. BMC Bioinform. 13(10), S16 (2012)CrossRefGoogle Scholar
  5. 5.
    UniProt Consortium, et al.: Uniprot: a hub for protein information. Nucleic Acids Res. 43, 989 (2014)Google Scholar
  6. 6.
    Dankelmann, P., Volkmann, L.: New sufficient conditions for equality of minimum degree and edge-connectivity. Ars Combinatoria 40, 270–278 (1995)MathSciNetMATHGoogle Scholar
  7. 7.
    Friedberg, I.: Automated protein function prediction the genomic challenge. Briefings Bioinform. 7(3), 225–242 (2006)CrossRefGoogle Scholar
  8. 8.
    Geva, G., Sharan, R.: Identification of protein complexes from co-immunoprecipitation data. Bioinformatics 27(1), 111–117 (2011)CrossRefGoogle Scholar
  9. 9.
    Hartuv, E., Schmitt, A.O., Lange, J., Meier-Ewert, S., Lehrach, H., Shamir, R.: An algorithm for clustering cDNA fingerprints. Genomics 66(3), 249–256 (2000)CrossRefGoogle Scholar
  10. 10.
    Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4), 175–181 (2000)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Hüffner, F., Komusiewicz, C., Liebtrau, A., Niedermeier, R.: Partitioning biological networks into highly connected clusters with maximum edge coverage. IEEE/ACM Trans. Comput. Biol. Bioinf. (TCBB) 11(3), 455–467 (2014)CrossRefGoogle Scholar
  12. 12.
    Hüffner, F., Komusiewicz, C., Sorge, M.: Finding highly connected subgraphs. In: Italiano, G.F., Margaria-Steffen, T., Pokorný, J., Quisquater, J.-J., Wattenhofer, R. (eds.) SOFSEM 2015-Testing. LNCS, vol. 8939, pp. 254–265. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, New York (1972)CrossRefGoogle Scholar
  14. 14.
    Pržulj, N., Wigle, D.A., Jurisica, I.: Functional topology in a network of protein interactions. Bioinformatics 20(3), 340–348 (2004)CrossRefGoogle Scholar
  15. 15.
    Radivojac, P., Clark, W.T., Oron, T.R., Schnoes, A.M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al.: A large-scale evaluation of computational protein function prediction. Nat. Methods 10(3), 221–227 (2013)CrossRefGoogle Scholar
  16. 16.
    Rentzsch, R., Orengo, C.A.: Protein function prediction-the power of multiplicity. Trends Biotechnol. 27(4), 210–219 (2009)CrossRefGoogle Scholar
  17. 17.
    Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Mol. Syst. Biol. 3(1), 88 (2007)Google Scholar
  18. 18.
    Wang, L.: Near optimal solutions for maximum quasi-bicliques. J. Comb. Optim. 25(3), 481–497 (2013)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jucheol Moon
    • 1
  • Iddo Friedberg
    • 2
  • Oliver Eulenstein
    • 1
  1. 1.Department of Computer ScienceIowa State UniversityAmesUSA
  2. 2.Department of Veterinary Microbiology and Preventive MedicineIowa State UniversityAmesUSA

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