Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 363408, 11 pages
http://dx.doi.org/10.1155/2014/363408
Research Article

Differential Protein Network Analysis of the Immune Cell Lineage

1Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway
2Biomedical Research Group, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, 0310 Oslo, Norway
3Institute of Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway

Received 18 April 2014; Revised 28 June 2014; Accepted 12 July 2014; Published 21 September 2014

Academic Editor: Filippo Castiglione

Copyright © 2014 Trevor Clancy and Eivind Hovig. 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. C. Benoist, L. Lanier, M. Merad, and D. Mathis, “Consortium biology in immunology: the perspective from the immunological genome project,” Nature Reviews Immunology, vol. 12, no. 10, pp. 734–740, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. T. S. P. Heng, M. W. Painter, K. Elpek et al., “The immunological genome project: networks of gene expression in immune cells,” Nature Immunology, vol. 9, no. 10, pp. 1091–1094, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. G. Hyatt, R. Melamed, R. Park et al., “Gene expression microarrays: glimpses of the immunological genome,” Nature Immunology, vol. 7, no. 7, pp. 686–691, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. C. C. Kim and L. L. Lanier, “Beyond the transcriptome: completion of act one of the immunological genome project,” Current Opinion in Immunology, vol. 25, no. 5, pp. 593–597, 2013. View at Publisher · View at Google Scholar
  5. T. Shay and J. Kang, “Immunological Genome Project and systems immunology,” Trends in Immunology, vol. 34, no. 12, pp. 602–609, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. J. L. Gardy, D. J. Lynn, F. S. L. Brinkman, and R. E. W. Hancock, “Enabling a systems biology approach to immunology: focus on innate immunity,” Trends in Immunology, vol. 30, no. 6, pp. 249–262, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel, “Discovering regulatory and signalling circuits in molecular interaction networks,” Bioinformatics, vol. 18, supplement 1, pp. S233–S240, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Ulitsky and R. Shamir, “Identification of functional modules using network topology and high-throughput data,” BMC Systems Biology, vol. 1, article 8, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. I. Ulitsky and R. Shamir, “Identifying functional modules using expression profiles and confidence-scored protein interactions,” Bioinformatics, vol. 25, no. 9, pp. 1158–1164, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Bossi and B. Lehner, “Tissue specificity and the human protein interaction network,” Molecular Systems Biology, vol. 5, article 260, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. V. Jojic, T. Shay, K. Sylvia et al., “Identification of transcriptional regulators in the mouse immune system,” Nature Immunology, vol. 14, no. 6, pp. 633–643, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Razick, G. Magklaras, and I. M. Donaldson, “iRefIndex: a consolidated protein interaction database with provenance,” BMC Bioinformatics, vol. 9, article 405, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Turner, S. Razick, A. L. Turinsky et al., “iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence,” Database, vol. 2010, Article ID baq023, 2010. View at Google Scholar · View at Scopus
  14. A. L. Turinsky, S. Razick, B. Turner, I. M. Donaldson, and S. J. Wodak, “Navigating the global protein-protein interaction landscape using iRefWeb,” Methods in Molecular Biology, vol. 1091, pp. 315–331, 2014. View at Google Scholar
  15. M. P. H. Stumpf, T. Thorne, E. De Silva et al., “Estimating the size of the human interactome,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 19, pp. 6959–6964, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. J. I. Fuxman Bass, A. Diallo, J. Nelson, J. M. Soto, C. L. Myers, and A. J. Walhout, “Using networks to measure similarity between genes: association index selection,” Nature Methods, vol. 10, no. 12, pp. 1169–1176, 2013. View at Google Scholar
  17. A. L. Barabási, “Scale-free networks: a decade and beyond,” Science, vol. 325, no. 5939, pp. 412–413, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A. Barabási, “Hierarchical organization of modularity in metabolic networks,” Science, vol. 297, no. 5586, pp. 1551–1555, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 23, pp. 8577–8582, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Clancy, E. A. Rødland, S. Nygard, and E. Hovig, “Predicting physical interactions between protein complexes,” Molecular and Cellular Proteomics, vol. 12, no. 6, pp. 1723–1734, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Y. Ahn, J. P. Bagrow, and S. Lehmann, “Link communities reveal multiscale complexity in networks,” Nature, vol. 466, no. 7307, pp. 761–764, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486, no. 3–5, pp. 75–174, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science, vol. 315, no. 5814, pp. 972–976, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. U. Bodenhofer, A. Kothmeier, and S. Hochreiter, “APCluster: an R package for affinity propagation clustering,” Bioinformatics, vol. 27, no. 17, pp. 2463–2464, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. J. Brusco and H. Köhn, “Comment on “clustering by passing messages between data points”,” Science, vol. 319, no. 5864, p. 726, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. B. J. Frey and D. Dueck, “Response to comment on “clustering by passing messages between data points”,” Science, vol. 319, no. 5864, p. 726, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Mézard, “Where are the exemplars?” Science, vol. 315, no. 5814, pp. 949–951, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. 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
  29. B. T. Sherman, D. W. Huang, Q. Tan et al., “DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis,” BMC Bioinformatics, vol. 8, article 426, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Kirov, R. Ji, J. Wang, and B. Zhang, “Functional annotation of differentially regulated gene set using WebGestalt: a gene set predictive of response to ipilimumab in tumor biopsies,” Methods in Molecular Biology, vol. 1101, pp. 31–42, 2014. View at Publisher · View at Google Scholar
  31. J. Wang, D. Duncan, Z. Shi, and B. Zhang, “WEB-based GEne SeT analysis toolkit (WebGestalt): update 2013,” Nucleic Acids Research, vol. 41, pp. W77–W83, 2013. View at Google Scholar
  32. P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software Environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Staerk and S. N. Constantinescu, “The JAK-STAT pathway and hematopoietic stem cells from the JAK2 V617F perspective,” JAK-STAT, vol. 1, no. 3, pp. 184–190, 2012. View at Publisher · View at Google Scholar
  34. M. M. Davis, “A prescription for human immunology,” Immunity, vol. 29, no. 6, pp. 835–838, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Clancy, M. Pedicini, F. Castiglione et al., “Immunological network signatures of cancer progression and survival,” BMC Medical Genomics, vol. 4, article 28, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. K. B. Lorvik, O. A. Haabeth, T. Clancy, B. Bogen, and A. Corthay, “Molecular profiling of tumor-specific T1 cells activated in vivo,” Oncoimmunology, vol. 2, no. 5, Article ID e24383, 2013. View at Google Scholar
  37. T. Shay, V. Jojic, O. Zuk et al., “Conservation and divergence in the transcriptional programs of the human and mouse immune systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 8, pp. 2946–2951, 2013. View at Publisher · View at Google Scholar · View at Scopus