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A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions

  • Arnon Mazza
  • Irit Gat-Viks
  • Hesso Farhan
  • Roded Sharan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8126)

Abstract

The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.

Keywords

Integer Linear Program Steiner Tree Anchor Node Integer Linear Program Formulation Network Analysis Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Beisser, D., Klau, G., Dandekar, T., Mueller, T., Dittrich, M.: BioNet an R-package for the functional analysis of biological networks. Bioinformatics 26, 1129–1130 (2010)CrossRefGoogle Scholar
  2. 2.
    Huang, S., Fraenkel, E.: Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal. 2(81), ra40 (2009)Google Scholar
  3. 3.
    Lotem, E., Riva, L., Su, L., Gitler, A., Cashikar, A., King, O., Auluck, P., Geddie, M., Valastyan, J., Karger, D., Lindquist, S., Fraenkel, E.: Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nature Genetics 41, 316–323 (2009)CrossRefGoogle Scholar
  4. 4.
    Yosef, N., Zalckvar, E., Rubinstein, A., Homilius, M., Atias, N., Vardi, L., Berman, I., Zur, H., Kimchi, A., Ruppin, E., Sharan, R.: ANAT: A tool for constructing and analyzing functional protein networks. Sci. Signal. 4 (2011)Google Scholar
  5. 5.
    Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Co. (1979)Google Scholar
  6. 6.
    Shapira, S., Gat-Viks, I., Shum, B., Dricot, A., Degrace, M., Liguo, W., Gupta, P., Hao, T., Silver, S., Root, D., Hill, D., Regev, A., Hacohen, N.: A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139(7), 1255–1267 (2009)CrossRefGoogle Scholar
  7. 7.
    Engelhardt, O., Sirma, H., Pandolfi, P., Haller, O.: Mx1 GTPase accumulates in distinct nuclear domains and inhibits influenza A virus in cells that lack promyelocytic leukaemia protein nuclear bodies. J. Gen. Virol. 85(8), 2315–2326 (2004)CrossRefGoogle Scholar
  8. 8.
    Wagner, S., Beli, P., Weinert, B., Nielsen, M., Cox, J., Mann, M., Choudhary, C.: A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol. Cell. Proteomics 10(10) (2011)Google Scholar
  9. 9.
    Watson, P., Townley, A., Koka, P., Palmer, K., Stephens, D.: Sec16 defines endoplasmic reticulum exit sites and is required for secretory cargo export in mammalian cells. Traffic 7(12), 1678–1687 (2006)CrossRefGoogle Scholar
  10. 10.
    Farhan, H., Wendeler, M., Mitrovic, S., Fava, E., Silberberg, Y., Sharan, R., Zerial, M., Hauri, H.: MAPK signaling to the early secretory pathway revealed by kinase/phosphatase functional screening. J. Cell. Biol. 189, 997–1011 (2010)CrossRefGoogle Scholar
  11. 11.
    Simpson, J., Joggerst, B., Laketa, V., Verissimo, F., Cetin, C., Erfle, H., Bexiga, M., Singan, V., Hériché, J., Neumann, B., Mateos, A., Blake, J., Bechtel, S., Benes, V., Wiemann, S., Ellenberg, J., Pepperkok, R.: Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway. Nat. Cell Biol. 14(7), 764–774 (2012)CrossRefGoogle Scholar
  12. 12.
    Ishihara, N., Hamasaki, M., Yokota, S., Suzuki, K., Kamada, Y., Kihara, A., Yoshimori, T., Noda, T., Ohsumi, Y.: Autophagosome requires specific early Sec proteins for its formation and NSF/SNARE for vacuolar fusion. Mol. Biol. Cell. 12(11), 3690–3702 (2001)CrossRefGoogle Scholar
  13. 13.
    Mizushima, N.: The role of the Atg1/ULK1 complex in autophagy regulation. Curr. Opin. Cell Biol. 22(2), 132–139 (2010)CrossRefGoogle Scholar
  14. 14.
    Hamasaki, M., Furuta, N., Matsuda, A., Nezu, A., Yamamoto, A., Fujita, N., Oomori, H., Noda, T., Haraguchi, T., Hiraoka, Y., Amano, A., Yoshimori, T.: Autophagosomes form at ER-mitochondria contact sites. Nature 495(7441), 389–393 (2013)CrossRefGoogle Scholar
  15. 15.
    Itakura, E., Mizushima, N.: Syntaxin 17: The autophagosomal SNARE. Autophagy 9(6) (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arnon Mazza
    • 1
  • Irit Gat-Viks
    • 2
  • Hesso Farhan
    • 3
  • Roded Sharan
    • 1
  1. 1.Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Dept. of Cell Research and ImmunologyTel Aviv UniversityTel AvivIsrael
  3. 3.Biotechnology Institute ThurgauUniversity of KonstanzKreuzlingenSwitzerland

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