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A New Framework for Bridging the Gap from Protein-Protein Interactions to Biological Process Interactions

  • Christos Dimitrakopoulos
  • Andreas Dimitris Vlantis
  • Konstantinos Theofilatos
  • Spiros Likothanassis
  • Seferina Mavroudi
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 437)

Abstract

Proteins and their interactions have been proven to play a central role in many cellular processes and have been extensively studied so far. However of great importance, little work has been conducted for the identification of biological process interactions in the higher cellular level which could provide knowledge about the high level cellular functionalities and maybe enable researchers to explain mechanisms that lead to diseases. Existing computational approaches for predicting Biological Process interactions used PPI graphs of low quality and coverage but failed to utilize weighted PPI graphs to quantify the quality of the interactions. In the present paper, we propose a unified two-step framework to reach the goal of predicting biological process interactions. After conducting a comparative study we selected as a first step the EVOKALMA model as a very promising algorithm for robust PPI prediction and scoring. Then, in order to be able to handle weights, we combined it with a novel variation of an existing algorithm for predicting biological processes interactions. The overall methodology was applied for predicting biological processes interactions for Saccharomyces Cerevisiae and Homo Sapiens organisms, uncovering thousands of interactions for both organisms. Most of the linked processes come in agreement with the existing knowledge but many of them should be further studied.

Keywords

Protein-Protein Interactions protein-protein interaction networks Biological Process Interactions EvoKalma Model protein function statistical enrichment 

References

  1. 1.
    Theofilatos, K., Dimitrakopoulos, C., Tsakalidis, A., Likothanassis, S., Papadimitriou, S., Mavroudi, S.: Computational Approaches for the Prediction of Protein-Protein Interactions: A Survey. Current Bioinformatics 6(4), 398–414 (2011)CrossRefGoogle Scholar
  2. 2.
    Dotan-Cohen, D., Letovsky, S., Melkman, A.A., Kasif, S.: Biological Process LinkageNetworks. PLoS ONE 4(4), e5313 (2009), doi:10.1371/journal.pone.0005313Google Scholar
  3. 3.
    Lasher, C.D., Rajagopalan, P., Murali, T.M.: Discovering networks of perturbed biological processes in hepatocyte cultures. PLoS ONE 6(1), e15247 (2011)Google Scholar
  4. 4.
    Lasher, C., Rajagopalan, P., Murali, T.M.: Summarizing cellular responses as biological process networks. BMC Systems Biology (2013), http://dx.doi.org/10.1186/1752-0509-7-68
  5. 5.
    Theofilatos, K., Dimitrakopoulos, C., Likothanassis, S., Kleftogiannis, D., Moschopoulos, C., Alexakos, C., Papadimitriou, S., Mavroudi, S.: The Human Interactome Knowledge Base (HINT-KB): An integrative Human protein interaction database enriched with predicted protein protein interaction scores using a novel hybrid technique (Evolutionary Kalman Mathematical Modelling - EvoKalMaModel). Artificial Intelligence Review, 1–17 (2013), , doi: 10.1007/s10462-013-9409-8Google Scholar
  6. 6.
    Keshava Prasad, T.S., Goel, R., Kandasamy, K., et al.: Human Protein Reference Database-2009 update. Nucleic Acids Res. 37, D767–D772 (2009)Google Scholar
  7. 7.
    Razick, S., Magklaras, G., Donaldson, I.M.: iRefIndex: A consolidated protein interaction database with provenance. BMC Bioinformatics 9(1), 405 (2008)CrossRefGoogle Scholar
  8. 8.
    Troyanskaya, O., Cantor, M., Sherlock, G., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)CrossRefGoogle Scholar
  9. 9.
    The UniProt Consortium: Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 40, D71-D75 (2012)Google Scholar
  10. 10.
    Stark, C., Breitkreutz, B., Reguly, T., et al.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535-D539 (2006)Google Scholar
  11. 11.
    Theofilatos, K.A., Dimitrakopoulos, C.M., Tsakalidis, A.K., et al.: A new hybrid method for predicting protein interactions using Genetic Algorithms and Extended Kalman Filters. In: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine (ITAB). art. no. 5687765 (2010), doi : 10.1109/ITAB.2010.5687765Google Scholar
  12. 12.
    Dimitrakopoulos, C.M., Theofilatos, K.A., Georgopoulos, E.F., et al.: Efficient Computational Construction of Weighted Protein-Protein Interaction Networks Using Adaptive Filtering Techniques Combined with Natural-Selection Based Heuristic Algorithms. International Journal of Systems Biology and Biomedical Technologies (IJSBBT) 1(2), 20–34 (2011)CrossRefGoogle Scholar
  13. 13.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill (1995)Google Scholar
  14. 14.
    Ashburner, M., Ball, C.A., Blake, J.A., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000)Google Scholar
  15. 15.
    Scott, M., Barton, G.: Probabilistic prediction and ranking of human protein-protein interactions. BMC Bioinformatics 8, 239 (2007)CrossRefGoogle Scholar
  16. 16.
    Zhang, Q., Petrey, D., Garzon, J., et al.: PrePPI: a structure-informed database of protein-protein interactions. Nucl. Acids Res (2012), doi:10.1093/nar/gks1231Google Scholar
  17. 17.
    Liu, Y., Kim, I., Zhao, H.: Protein interaction predictions from diverse sources. Drug Discov. Today 13, 409–416 (2008)CrossRefGoogle Scholar
  18. 18.
    Theofilatos, K., Dimitrakopoulos, C., Antoniou, M., Georgopoulos, E., Papadimitriou, S., Likothanassis, S., Mavroudi, S.: Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology. In: Jayne, C., Yue, S., Iliadis, L. (eds.) EANN 2012. CCIS, vol. 311, pp. 472–481. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Holland, J.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge (1995)Google Scholar
  20. 20.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  21. 21.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Veenman, C.J., Tax, D.M.: LESS: a model-based classifier for sparse subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1496–1500 (2005)CrossRefGoogle Scholar
  23. 23.
    Unal, E., Arbel-Eden, A., Sattler, U., Shroff, R., et al.: DNA damage response pathway uses histone modification to assemble a double-strand break-specific cohesin domain. Mol. Cell. 16, 991–1002 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Christos Dimitrakopoulos
    • 1
  • Andreas Dimitris Vlantis
    • 1
  • Konstantinos Theofilatos
    • 1
  • Spiros Likothanassis
    • 1
  • Seferina Mavroudi
    • 1
    • 2
  1. 1.Department of Computer Engineering and InformaticsUniversity of PatrasGreece
  2. 2.Department of Social Work, School of Sciences of Health and CareTechnological Educational Institute of Western GreeceGreece

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