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The Scientific World Journal
Volume 2012, Article ID 315797, 14 pages
http://dx.doi.org/10.1100/2012/315797
Research Article

Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

1Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan
2Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan

Received 9 September 2011; Accepted 17 October 2011

Academic Editor: Shanker Kalyana-Sundaram

Copyright © 2012 Cheng-Yu Yeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Segal, H. Wang, and D. Koller, “Discovering molecular pathways from protein interaction and gene expression data,” Bioinformatics, vol. 19, no. 1, pp. i264–i272, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. R. G. Michael and S. J. David, Computers and Intractability, A Guide to the Theory of NP-Completeness, W. H. Freeman, New York, NY, USA, 1990.
  3. M. Steffen, A. Petti, J. Aach, P. D'haeseleer, and G. Church, “Automated modelling of signal transduction networks,” BMC Bioinformatics, vol. 3, article 34, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Alon, R. Yuster, and U. Zwick, “Color-coding,” Journal of ACM, vol. 42, no. 4, pp. 844–856, 1995. View at Google Scholar
  5. T. Shlomi, D. Segal, E. Ruppin, and R. Sharan, “QPath: a method for querying pathways in a protein-protein interaction network,” BMC Bioinformatics, vol. 7, article 199, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. I. Mayrose, T. Shlomi, and N. D. Rubinstein, “Epitope mapping using combinational phase-display libraries: a grpah-based algorithm,” Nucleic Acids Research, vol. 35, no. 1, pp. 69–78, 2007. View at Google Scholar
  7. J. Scott, T. Ideker, R. M. Karp, and R. Sharan, “Efficient algorithms for detecting signaling pathways in protein interaction networks,” in Proceedings of the 9th Annual International Conference on Research in Computational Molecular Biology (RECOMB '05), vol. 3500 of Lecture Notes in Bioinformatics, pp. 1–13, Cambridge, Mass, USA, May 2005.
  8. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. F. Hüffner, S. Wernicke, and T. Zichner, “Algorithm engineering for color-coding with applications to signaling pathway detection,” Algorithmica, vol. 52, no. 2, pp. 114–132, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Zhao, R. Wang, L. Chen, and K. Aihara, “Automatic modeling of signal pathways from protein-protein interaction networks,” Asia Pacific Bioinformatics Conference, vol. 3, no. 42, pp. 287–296, 2007. View at Google Scholar
  11. G. Bebek and J. Yang, “PathFinder: mining signal transduction pathway segments from protein-protein interaction networks,” BMC Bioinformatics, vol. 8, article 335, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. E. Dolan, L. Ni, E. Camon, and J. A. Blake, “A procedure for assessing GO annotation consistency,” Bioinformatics, vol. 21, no. 1, pp. i136–i143, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Troyanskaya, M. Cantor, G. Sherlock et al., “Missing value estimation methods for DNA microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520–525, 2001. View at Google Scholar · View at Scopus
  14. C. Prieto and J. De Las Rivas, “APID: agile protein interaction DataAnalyzer,” Nucleic Acids Research, vol. 34, pp. W298–W302, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. H. C. Liu, C. R. Arias, and V. W. Soo, “BioIR: an approach to public domain resource integration of human protein-protein interaction,” in Proceeding of the 7th Asia Pacific Bioinformatics Conference (APBC '9), 2009.
  16. A. L. Barabási and Z. N. Oltvai, “Network biology: understanding the cell's functional organization,” Nature Reviews Genetics, vol. 5, no. 2, pp. 101–113, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Grigoriev, “A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae,” Nucleic Acids Research, vol. 29, no. 17, pp. 3513–3519, 2001. View at Google Scholar · View at Scopus
  19. J. Zhu, B. Zhang, E. N. Smith et al., “Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks,” Nature Genetics, vol. 40, no. 7, pp. 854–861, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, New York, NY, USA, 1995.
  21. S. Grossmann, S. Bauer, P. N. Robinson, and M. Vingron, “An improved statistic for detecting over-represented gene ontology annotations in gene sets,” in Proceedings of the 10th International Conference on Research in Computational Molecular Biology, vol. 3909 of Lecture Notes in Computer Science, pp. 85–98, 2006. View at Publisher · View at Google Scholar
  22. I. Xenarios, D. W. Rice, L. Salwinski, M. K. Baron, E. M. Marcotte, and D. Eisenberg, “DIP: the database of interacting proteins,” Nucleic Acids Research, vol. 28, no. 1, pp. 289–291, 2000. View at Google Scholar · View at Scopus
  23. Mega Yeast Gene Expression Data, http://gasch.genetics.wisc.edu/datasets.html.
  24. J. Lapointe, C. Li, J. P. Higgins et al., “Gene expression profiling identifies clinically relevant subtypes of prostate cancer,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 3, pp. 811–816, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. G. Sherlock, T. Hernandez-Boussard, A. Kasarskis et al., “The stanford microarray database,” Nucleic Acids Research, vol. 29, no. 1, pp. 152–155, 2001. View at Google Scholar · View at Scopus
  26. A. Nern and R. A. Arkowitz, “A Cdc24p-Far1p-Gβγ protein complex required for yeast orientation during mating,” Journal of Cell Biology, vol. 144, no. 6, pp. 1187–1202, 1999. View at Publisher · View at Google Scholar · View at Scopus
  27. H. W. Mewes, C. Amid, R. Arnold et al., “MIPS: analysis and annotation of proteins from whole genomes,” Nucleic Acids Research, vol. 32, pp. D41–D44, 2004. View at Google Scholar
  28. Y. Matsui, R. Matsui, R. Akada, and A. Toh-e, “Yeast src homology region 3 domain-binding proteins involved in bud formation,” Journal of Cell Biology, vol. 133, no. 4, pp. 865–878, 1996. View at Publisher · View at Google Scholar · View at Scopus
  29. E. Blackwell, I. M. Halatek, H. J. N. Kim, A. T. Ellicott, A. A. Obukhov, and D. E. Stone, “Effect of the pheromone-responsive Gα and phosphatase proteins of Saccharomyces cerevisiae on the subcellular localization of the Fus3 mitogen-activated protein kinase,” Molecular and Cellular Biology, vol. 23, no. 4, pp. 1135–1150, 2003. View at Publisher · View at Google Scholar · View at Scopus
  30. L. R. Kao, J. Peterson, R. Ji, L. Bender, and A. Bender, “Interactions between the ankyrin repeat-containing protein Akr1p and the pheromone response pathway in Saccharomyces cerevisiae,” Molecular and Cellular Biology, vol. 16, no. 1, pp. 168–178, 1996. View at Google Scholar · View at Scopus
  31. H. O. Park and E. Bi, “Central roles of small GTPases in the development of cell polarity in yeast and beyond,” Microbiology and Molecular Biology Reviews, vol. 71, no. 1, pp. 48–96, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. D. I. Johnson, “Cdc42: an essential Rho-type GTPase controlling eukaryotic cell polarity,” Microbiology and Molecular Biology Reviews, vol. 63, no. 1, pp. 54–105, 1999. View at Google Scholar · View at Scopus
  33. S. P. Palecek, A. S. Parikh, and S. J. Kron, “Sensing, signalling and integrating physical processes during Saccharomyces cerevisiae invasive and filamentous growth,” Microbiology, vol. 148, no. 4, pp. 893–907, 2002. View at Google Scholar · View at Scopus
  34. D. E. Levin, “Cell wall integrity signaling in Saccharomyces cerevisiae,” Microbiology and Molecular Biology Reviews, vol. 69, no. 2, pp. 262–291, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. H. Jeong, S. P. Mason, A. L. Barabási, and Z. N. Oltvai, “Lethality and centrality in protein networks,” Nature, vol. 411, no. 6833, pp. 41–42, 2001. View at Publisher · View at Google Scholar · View at Scopus
  36. J. Amberger, C. A. Bocchini, A. F. Scott, and A. Hamosh, “McKusick's Online Mendelian Inheritance in Man (OMIM®),” Nucleic Acids Research, vol. 37, no. 1, pp. D793–D796, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, “The KEGG resource for deciphering the genome,” Nucleic Acids Research, vol. 32, pp. D277–D280, 2004. View at Google Scholar · View at Scopus
  38. L. C. Li, H. Zhao, H. Shiina, C. J. Kane, and R. Dahiya, “PGDB: a curated and integrated database of genes related to the prostate,” Nucleic Acids Research, vol. 31, no. 1, pp. 291–293, 2003. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Wang, D. Yu, S. Agrawal, and R. Zhang, “Experimental therapy of human prostate cancer by inhibiting MDM2 expression with novel mixed-backbone antisense oligonucleotides: in vitro and in vivo activities and mechanisms,” Prostate, vol. 54, no. 3, pp. 194–205, 2003. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. Zhang, M. Li, H. Wang, S. Agrawal, and R. Zhang, “Antisense therapy targeting MDM2 oncogene in prostate cancer: effects on proliferation, apoptosis, multiple gene expression, and chemotherapy,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 20, pp. 11636–11641, 2003. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Sarfaraz, F. Afaq, V. M. Adhami, A. Malik, and H. Mukhtar, “Cannabinoid receptor agonist-induced apoptosis of human prostate cancer cells LNCaP proceeds through sustained activation of ERK1/2 leading to G 1 cell cycle arrest,” The Journal of Biological Chemistry, vol. 281, no. 51, pp. 39480–39491, 2006. View at Publisher · View at Google Scholar · View at Scopus