Multivariate Networks in the Life Sciences

  • Oliver Kohlbacher
  • Falk Schreiber
  • Matthew O. Ward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8380)


Bioinformatics can be defined as the development and use of computational methods to solve problems from the life sciences. With the advent of omics technologies, the flood of biological data has been growing exponentially, and the traditional manual analysis and exploration of biological data is less and less an option. Networks are a powerful abstraction that can be utilized to structure, explore, and analyze biological data on different levels: from the atomic details to cellular processes to evolutionary relationships. In this chapter, we will introduce the basic characteristics of the different types of biological networks, give examples of actual visualizations, and discuss current challenges.


Metabolic Network Biological Network Heterogeneous Network Omics Data Nucleic Acid Research 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albrecht, M., Kerren, A., Klein, K., Kohlbacher, O., Mutzel, P., Paul, W., Schreiber, F., Wybrow, M.: On open problems in biological network visualization. In: Eppstein, D., Gansner, E.R. (eds.) GD 2009. LNCS, vol. 5849, pp. 256–267. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Bader, G.D., Betel, D., Hogue, C.W.: BIND: the biomolecular interaction network database. Nucleic Acids Research 31(1), 248–250 (2003)CrossRefGoogle Scholar
  3. 3.
    Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nature Reviews Genetics 5(2), 101–113 (2004)CrossRefGoogle Scholar
  4. 4.
    Chen, H., Sharp, B.M.: Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics 5(1), 147 (2004)CrossRefGoogle Scholar
  5. 5.
    Gehlenborg, N., O’Donoghue, S.I., Baliga, N.S., Goesmann, A., Hibbs, M.A., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., Gavin, A.C.: Visualization of omics data for systems biology. Nature Methods 7, S56–S68 (2010)Google Scholar
  6. 6.
    Junker, A., Rohn, H., Schreiber, F.: Visual analysis of transcriptome data in the context of anatomical structures and biological networks. Frontiers in Plant Science 3, 252 (2012)CrossRefGoogle Scholar
  7. 7.
    Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., Hirakawa, M.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Research 34, D354–D357 (2006)Google Scholar
  8. 8.
    Kerren, A., Schreiber, F.: Toward the role of interaction in visual analytics. In: Rose, O., Uhrmacher, A.M. (eds.) Proceedings of the Winter Simulation Conference (WSC 2012). pp. 420:1–420:13 (2012)Google Scholar
  9. 9.
    Kerren, A., Schreiber, F.: Network visualization for integrative bioinformatics. In: Approaches in Integrative Bioinformatics: Towards the Virtual Cell, pp. 173–202. Springer (2014)Google Scholar
  10. 10.
    Kono, N., Arakawa, K., Ogawa, R., Kido, N., Oshita, K., Ikegami, K., Tamaki, S., Tomita, M.: Pathway projector: Web-based zoomable pathway browser using KEGG atlas and Google maps API. PLoS One 4(11), e7710 (2009)Google Scholar
  11. 11.
    Koschützki, D.: Network centralities. In: Junker, B.H., Schreiber, F. (eds.) Analysis of Biological Networks. Wiley Series on Bioinformatics, Computational Techniques and Engineering, pp. 65–84. Wiley (2008)Google Scholar
  12. 12.
    Le Novère, N., Hucka, M., Mi, H., Moodie, S., Schreiber, F., Sorokin, A., Demir, E., Wegner, K., Aladjem, M., Wimalaratne, S.M., Bergman, F.T., Gauges, R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Villéger, A., Boyd, S.E., Calzone, L., Courtot, M., Dogrusoz, U., Freeman, T., Funahashi, A., Ghosh, S., Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle, S., Schmidt, E., Watterson, S., Wu, G., Goryanin, I., Kell, D.B., Sander, C., Sauro, H., Snoep, J.L., Kohn, K., Kitano, H.: The systems biology graphical notation. Nature Biotechnology 27, 735–741 (2009)CrossRefGoogle Scholar
  13. 13.
    Matthews, L., Gopinath, G., Gillespie, M., Caudy, M., Croft, D., de Bono, B., Garapati, P., Hemish, J., Hermjakob, H., Jassal, B., Kanapin, A., Lewis, S., Mahajan, S., May, B., Schmidt, E., Vastrik, I., Wu, G., Birney, E., Stein, L., D’Eustachio, P.: Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Research 37(1), D619–D622 (2009)Google Scholar
  14. 14.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002)CrossRefGoogle Scholar
  15. 15.
    Rohn, H., Hartmann, A., Junker, A., Junker, B.H., Schreiber, F.: FluxMap: a Vanted add-on for the visual exploration of flux distributions in biological networks. BMC Systems Biology 6, 33 (2012)Google Scholar
  16. 16.
    Rohn, H., Junker, A., Hartmann, A., Grafahrend-Belau, E., Treutler, H., Klapperstck, M., Czauderna, T., Klukas, C., Schreiber, F.: VANTED v2: a framework for systems biology applications. BMC Systems Biology 6(139) (2012)Google Scholar
  17. 17.
    Rohn, H., Klukas, C., Schreiber, F.: Creating views on integrated multidomain data. Bioinformatics 27(13), 1839–1845 (2011)CrossRefGoogle Scholar
  18. 18.
    Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The database of interacting proteins: 2004 update. Nucleic Acids Research 32(1), 449–451 (2004)CrossRefGoogle Scholar
  19. 19.
    Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.P.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102(43), 15545–15550 (2005)CrossRefGoogle Scholar
  20. 20.
    Truco, M.J., Ashrafi, H., Kozik, A., van Leeuwen, H., Bowers, J., Wo, S.R.C., Stoffel, K., Xu, H., Hill, T., Van Deynze, A., et al.: An ultra-high-density, transcript-based, genetic map of lettuce. G3: Genes| Genomes| Genetics 3(4), 617–631 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Oliver Kohlbacher
  • Falk Schreiber
  • Matthew O. Ward

There are no affiliations available

Personalised recommendations