Skip to main content

Efficiently Enumerating All Connected Induced Subgraphs of a Large Molecular Network

  • Conference paper
Algorithms for Computational Biology (AlCoB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8542))

Included in the following conference series:

  • 1254 Accesses

Abstract

In systems biology, the solution space for a broad range of problems is composed of sets of functionally associated biomolecules. Since connectivity in molecular interaction networks is an indicator of functional association, such sets can be identified from connected induced subgraphs of molecular interaction networks. Applications typically quantify the relevance (e.g., modularity, conservation, disease association) of connected subnetworks using an objective function and use a search algorithm to identify sets of subnetworks that maximize this objective function. Efficient enumeration of connected subgraphs of a large graph is therefore useful for these applications, and many existing search algorithms can be used for this purpose. However, there is a lack of non-heuristic algorithms that minimize the total number of subgraphs evaluated during the search for subgraphs that maximize the objective function. Here, we propose and evaluate an algorithm that reduces the computations necessary to enumerate subgraphs that maximize an objective function given a monotonically decreasing bounding function.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Avis, D., Fukuda, K.: Reverse search for enumeration. Discrete Applied Mathematics (1993)

    Google Scholar 

  2. Bollobas, B.: Hereditary properties of graphs asymptotic enumeration global structure and colouring. Documenta Mathematica, 333–342 (1998)

    Google Scholar 

  3. Chowdhury, S., Koyuturk, M.: Identification of coordinately dysregulated subnetworks in complex phenotypes. In: Berger, B. (ed.) Pacific Symposium on Biocomputing, pp. 133–144 (2010)

    Google Scholar 

  4. Chowdhury, S., Nibbe, R., Chance, M., Koyuturk, M.: Subnetwork state functions define dysregulated subnetworks in cancer. Journal of Computational Biology 18(3), 263–281 (2011)

    Article  MathSciNet  Google Scholar 

  5. Chuang, H.Y., Lee, E., Yu-Tsueng, L.D., Ideker, T.: Network-based classification of breast cancer metastasis. Molecular Systems Biology (2007)

    Google Scholar 

  6. Dao, P., Wang, K., Collins, C., Ester, M., Lapuk, A., Sahinalp1, S.C.: Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics (July 2011)

    Google Scholar 

  7. Flannick, J., Novak, A., Srinivasan, B., McAdams, H., Batzoglou, S.: Graemlin: General and robust alignment of multiple large interaction networks. Genome Research (2006)

    Google Scholar 

  8. Hopcroft, J., Tarjan, R.: Efficient algorithms for graph manipulation. Communications of the ACM 16(6) (1973)

    Google Scholar 

  9. Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl. 1), S233–S240 (2002), http://dx.doi.org/10.1093/bioinformatics/18.suppl_1.s233

  10. Jia, P., Zheng, S., Long, J., Zheng, W., Zhao, Z.: dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27(1), 95–102 (2011)

    Article  Google Scholar 

  11. Kalaev, M., Smoot, M., Ideker, T., Sharan, R.: Networkblast: comparative analysis of protein networks. Bioinformatics (2008)

    Google Scholar 

  12. Karakashian, S., Choueiry, B.Y., Hartke, S.G.: An algorithm for generating all connected subgraphs with k vertices of a graph (May 2013), http://www.math.unl.edu/~shartke2/math/papers/k-subgraphs.pdf

  13. Kesheva, P., et al.: Human protein reference database: 2009 update. Nucleic Acids Research 37, 767–772 (2009)

    Article  Google Scholar 

  14. Knuth, D.: The Art of Computer Programming, Combinatorial Algorithms Part 1, vol. 4. Addison-Wesley (2012)

    Google Scholar 

  15. Konga, B., Yanga, T., Chenb, L., Qin Kuanga, Y., Wen Gua, J., Xiaa, X., Chenga, L., Hai Zhang, J.: Proteinprotein interaction network analysis and gene set enrichment analysis in epilepsy patients with brain cancer. Journal of Clinical Neuroscience (2013)

    Google Scholar 

  16. Koyutürk, M., Kim, Y., Subramaniam, S., Szpankowski, W., Grama, A.: Detecting conserved interaction patterns in biological networks. Journal of Computational Biology (2006)

    Google Scholar 

  17. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (2007)

    Google Scholar 

  18. Patel, V., Gokulrangan, G., Chowdhury, S., Chen, Y., Sloan, A., Koyutrk, M., Barnholtz-Sloan, J., Chance, M.: Network signatures of survival in glioblastoma multiforme. PLOS Computational Biology 9 (2013)

    Google Scholar 

  19. Rymon, R.: Search through systematic set enumeration. Tech. rep., University of Pennsylvania (August 1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Maxwell, S., Chance, M.R., Koyutürk, M. (2014). Efficiently Enumerating All Connected Induced Subgraphs of a Large Molecular Network. In: Dediu, AH., Martín-Vide, C., Truthe, B. (eds) Algorithms for Computational Biology. AlCoB 2014. Lecture Notes in Computer Science(), vol 8542. Springer, Cham. https://doi.org/10.1007/978-3-319-07953-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07953-0_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07952-3

  • Online ISBN: 978-3-319-07953-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics