Studying Connectivity Properties in Human Protein–Protein Interaction Network in Cancer Pathway

  • Vera Tomaino
  • Ashwin Arulselvan
  • Pierangelo Veltri
  • Panos M. Pardalos
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 65)

Abstract

The critical node detection problem seeks a set of nodes with at most a given cardinality, whose deletion results in maximum pairwise disconnectivity. The critical nodes are responsible for the overall connectivity of the graph. In a prior work by the authors, a novel combinatorial algorithm is proposed to identify critical nodes in sparse graphs. The robustness of the algorithm is demonstrated on several test instances. In this work, we apply this algorithm on the human PPI network. In this article, the human protein–protein interaction (PPI) network is considered, where the nodes correspond to proteins and the edges correspond to the interaction between the proteins. The heuristic technique is applied to identify the critical nodes on a subgraph of the PPI network induced by a node set corresponding to the proteins that are present in the cancer pathway in the human PPI network. These set of proteins are obtained from the Human Cancer Protein Interaction Network (HCPIN) database. The information about the interactions between these proteins are obtained from the Human Protein Resource Database (HPRD), in order to construct the graph. The critical nodes in the human cancer protein network correspond to the hub proteins that are responsible for the overall connectivity of the graph and play a role in multiple biological processes. The dysfunction of the interactions with some of the hub proteins or mutation in these proteins have been directly linked to cancer and other diseases. In this research, such hub proteins were identified from a purely graph theoretic perspective in terms of their role in determining the overall connectivity of the PPI network. This new technique will shed light on new hub proteins that are yet to be discovered and the proteins responsible for other genetic disorders.

Keywords

Gene regulatory networks Critical node detection Node disconnectivity Cancer treatment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

 eferences

  1. 1.
    D. S. Aaronson and C. M. Horvath. A road map for those who don’t know jak–stat. Science, 296(5573):1653–1655.Google Scholar
  2. 2.
    R.K. Ahuja, T.L. Magnanti, and J.B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs, N.J. 1993.MATHGoogle Scholar
  3. 3.
    Tero Aittokallio and Benno Schwikowski. Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics, 7(3):243–255, 2006.CrossRefGoogle Scholar
  4. 4.
    Réka Albert. Scale-free networks in cell biology. Journal of Cell Science, 118:4947–4957, 2005.CrossRefGoogle Scholar
  5. 5.
    A. Arulselvan, C.W. Commander, L. Elefteriadou, and P.M. Pardalos. Detecting critical nodes in sparse graphs. Comput. Oper. Res., 36(7):2193–2200, 2009.MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    V. Boginski and C.W. Commander. Identifying critical nodes in protein–protein interaction networks, pp. 153–167. World Scientific, Singapore, 2008.Google Scholar
  7. 7.
    S.P. Borgatti. Identifying sets of key players in a network. Computational and Mathematical Organization Theory, 12:21–34, 2006.MATHCrossRefGoogle Scholar
  8. 8.
    T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, Cambridge, MA, 2001.MATHGoogle Scholar
  9. 9.
    G. Erkan A. Özgür, T. Vu and D.R. Radev. Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics, 24:277–85, 2008.CrossRefGoogle Scholar
  10. 10.
    R. Hoshino, Y. Chatani, T. Yamori, T. Tsuruo, H. Oka, O. Yoshida, Y. Shimada, S. Ari-i, H. Wada, J. Fujimoto, and M. Kohno. Constitutive activation of the 41-/43-kda mitogen-activated protein kinase signaling pathway in human tumors. Oncogene, 18(3):813–822, 1999.CrossRefGoogle Scholar
  11. 11.
    Thanh-Phuong Nguyen and Ferenc Jordan. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network. BMC Systems Biology, 4(1):103, 2010.Google Scholar
  12. 12.
    Kang Ning, Hoong Ng, Sriganesh Srihari, Hon Leong, and Alexey Nesvizhskii. Examination of the relationship between essential genes in ppi network and hub proteins in reverse nearest neighbor topology. BMC Bioinformatics, 11(1):505, 2010.Google Scholar
  13. 13.
    C.A.S. Oliveira, P.M. Pardalos, and T.M. Querido. Integer formulations for the message scheduling problem on controller area networks. In D. Grundel, R. Murphey, and P. Pardalos, editors, Theory and Algorithms for Cooperative Systems, pp. 353–365. World Scientific, Singapore, 2004.Google Scholar
  14. 14.
    M.R. Said, T.J. Begley, A. oppenheim, D. Lauffenberger, and L. Samson. Global network analysis of phenotypic effects: protein networks and toxicity modulation in saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA, vol. 101, pp. 18006–18011, 2004.CrossRefGoogle Scholar
  15. 15.
    A. Takaoka, S. Hayakawa, H. Yanai, D. Stoiber, H. Negishiand H. Kikuchi, S. Sasaki, and K. Imai. Integration of interferon-alpha/beta signalling to p53 responses in tumour suppression and antiviral defence. Nature, 424(6948):516–523, 2003.CrossRefGoogle Scholar
  16. 16.
    S. Wuchty and E. Almaas. Peeling the yeast protein network. Proteomics, 5:444–449, 2005.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Vera Tomaino
    • 1
  • Ashwin Arulselvan
    • 2
  • Pierangelo Veltri
    • 1
  • Panos M. Pardalos
    • 3
  1. 1.Department of Experimental Medicine and ClinicUniversity Magna Græcia of CatanzaroCatanzaroItaly
  2. 2.DIMAP, Warwick Business SchoolUniversity of WarwickCoventryUK
  3. 3.Industrial and Systems Engineering DepartmentUniversity of FloridaGainesvilleUSA

Personalised recommendations