, Volume 195, Issue 4, pp 1751–1777 | Cite as

Network analyses in systems biology: new strategies for dealing with biological complexity

  • Sara GreenEmail author
  • Maria Şerban
  • Raphael Scholl
  • Nicholaos Jones
  • Ingo Brigandt
  • William Bechtel


The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.


Network modeling Systems biology Biological networks Representation Mechanistic research strategies 



This paper was initiated while the authors were fellows at the Center for Philosophy of Science, University of Pittsburgh. We would like to thank the Center for providing us such a rich environment that was conducive to establishing this collaboration. We also thank three anonymous referees for their critical suggestions. Ingo Brigandt’s work is also supported by the Social Sciences and Humanities Research Council of Canada (Insight Grant 435-2016-0500).


  1. Alon, U. (2007). An introduction to systems biology: Design principles of biological circuits. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
  2. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.Google Scholar
  3. Beber, M. E., Fretter, C., Jain, S., Sonnenschein, N., Muller-Hannemann, M., & Hutt, M. T. (2012). Artefacts in statistical analyses of network motifs: General framework and application to metabolic networks. Journal of the Royal Society Interface, 9, 3426–3435.CrossRefGoogle Scholar
  4. Bechtel, W. (2011). Mechanism and biological explanation. Philosophy of Science, 78, 533–557.CrossRefGoogle Scholar
  5. Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421–441.CrossRefGoogle Scholar
  6. Bechtel, W., & Richardson, R. C. (1993/2010). Discovering complexity: Decomposition and localization as strategies in scientific research. Cambridge, MA: MIT Press. 1993 edition published by Princeton University Press.Google Scholar
  7. Brigandt, I., Green, S., & O’Malley, M. A. (2017). Systems biology and mechanistic explanation. In S. Glennan & P. Illari (Eds.), The Routledge handbook of mechanisms and mechanical philosophy. New York, NY: Routledge.Google Scholar
  8. Chatr-aryamontri, A., Ceol, A., Licata, L., & Cesareni, G. (2008). Protein interactions: Integration leads to belief. Trends in Biochemical Sciences, 33, 241–242.CrossRefGoogle Scholar
  9. Craver, C. F., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. Chicago: University of Chicago Press.Google Scholar
  10. Creixell, P., Schoof, E. M., Erler, J. T., & Linding, R. (2012). Navigating cancer network attractors for tumor-specific therapy. Nature Biotechnology, 30, 842–848.CrossRefGoogle Scholar
  11. De Lichtenberg, U., Jensen, L. J., Brunak, S., & Bork, P. (2005). Dynamic complex formation during the yeast cell cycle. Science, 307, 724–727.Google Scholar
  12. Green, S. (2015). Revisiting generality in biology: Systems biology and the quest for design principles. Biology & Philosophy, 30(5), 629–652.CrossRefGoogle Scholar
  13. Green, S., Levy, A., & Bechtel, W. (2015). Design sans adaptation. European Journal for Philosophy of Science, 5, 15–29.CrossRefGoogle Scholar
  14. Gross, F. (2011). What systems biology can tell us about disease. History and Philosophy of the Life Sciences, 33, 477–496.Google Scholar
  15. Huang, S. (2004). Back to the biology in systems biology: What can we learn from biomolecular networks? Briefings in Functional Genomics and Proteomics, 2, 279–297.CrossRefGoogle Scholar
  16. Huang, S. (2011). Systems biology of stem cells: Three useful perspectives to help overcome the paradigm of linear pathways. Philosophical Transactions of the Royal Society B: Biolological Sciences, 366, 2247–2259.CrossRefGoogle Scholar
  17. Huang, S. (2013). Genetic and non-genetic instability in tumor progression: Link between the fitness landscape and the epigenetic landscape of cancer cells. Cancer and Metastasis Reviews, 32, 423–448.CrossRefGoogle Scholar
  18. Huang, S., & Kauffman, S. (2013). How to escape the cancer attractor: Rationale and limitations of multi-target drugs. Seminars in Cancer Biology, 23, 270–278.CrossRefGoogle Scholar
  19. Huang, S., Eichler, G., Bar-Yam, Y., & Ingber, D. E. (2005). Cell fates as high-dimensional attractor states of a complex gene regulatory network. Physical Review Letters, 94, 128701.CrossRefGoogle Scholar
  20. Huang, S., Ernberg, I., & Kauffman, S. A. (2009). Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Seminars in Cell & Developmental Biology, 20, 869–876.CrossRefGoogle Scholar
  21. Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177, 213–245.CrossRefGoogle Scholar
  22. Isalan, M., Lemerle, C., Michalodimitrakis, K., Horn, C., Beltrao, P., Raineri, E., et al. (2008). Evolvability and hierarchy in rewired bacterial gene networks. Nature, 452, 840–845.CrossRefGoogle Scholar
  23. Jaeger, J., & Crombach, A. (2012). Life’s attractors: Understanding developmental systems through reverse engineering and in silico evolution. Advances in Experimental Medicine and Biology, 751, 93–119.CrossRefGoogle Scholar
  24. Jaeger, J., & Sharpe, J. (2014). On the concept of mechanism in development. In A. Minelli & T. Pradeu (Eds.), Towards a theory of development (pp. 56–78). Oxford: Oxford University Press.CrossRefGoogle Scholar
  25. Janes, K. A., Albeck, J. G., Gaudet, S., Sorger, P. K., Lauffenburger, D. A., & Yaffe, M. B. (2005). A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science, 310, 1646–1653.CrossRefGoogle Scholar
  26. Jones, N. (2014). Bowtie structures, pathway diagrams, and topological explanation. Erkenntnis, 79, 1135–1155.CrossRefGoogle Scholar
  27. Kaiser, M. I. (2015). Reductive explanation in the biological sciences. Cham: Springer.CrossRefGoogle Scholar
  28. Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22, 437–467.CrossRefGoogle Scholar
  29. Kauffman, S. A. (1971). Differentiation of malignant to benign cells. Journal of Theoretical Biology, 31, 429–451.CrossRefGoogle Scholar
  30. Lang, J. Y., Shi, Y., & Chin, Y. E. (2013). Reprogramming cancer cells: Back to the future. Oncogene, 32, 2247–2248.CrossRefGoogle Scholar
  31. Lee, M. J., Ye, A. S., Gardino, A. K., Heijink, A. M., Sorger, P. K., MacBeath, G., et al. (2012). Sequential application of anti-cancer drugs enhances cell death by re-wiring apoptotic signaling networks. Cell, 149, 780–794.CrossRefGoogle Scholar
  32. Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80, 241–261.CrossRefGoogle Scholar
  33. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.CrossRefGoogle Scholar
  34. Mackay, J. P., Sunde, M., Lowry, J. A., Crossley, M., & Matthews, J. M. (2007). Protein interactions: Is seeing believing? Trends in Biochemical Sciences, 32, 530–531.CrossRefGoogle Scholar
  35. Mangan, S., Zaslaver, A., & Alon, U. (2003). The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. Journal of Molecular Biology, 334, 197–204.Google Scholar
  36. McAdams, H. H., & Shapiro, L. (2003). A bacterial cell-cycle regulatory network operating in time and space. Science, 301, 1874–1877.CrossRefGoogle Scholar
  37. Newman, M. E. J. (2010). Networks: An introduction. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  38. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297, 1551–1555.CrossRefGoogle Scholar
  39. Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. (2002). Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31, 64–68.CrossRefGoogle Scholar
  40. Tyson, J. J., & Novak, B. (2010). Functional motifs in biochemical reaction networks. Annual Review of Physical Chemistry, 61, 219–240.CrossRefGoogle Scholar
  41. Waddington, C. H. (1940). Organisers and genes. Cambridge: Cambridge University Press.Google Scholar
  42. Waddington, C. H. (1953). The interactions of some morphogenetic genes in Drosophila melanogaster. Journal of Genetics, 51, 243–258.CrossRefGoogle Scholar
  43. Wagner, A., & Fell, D. (2001). The small world inside large metabolic networks. Biological Sciences: Proceedings of the Royal Society of London B, 280, 1803–1810.Google Scholar
  44. Watts, D., & Strogatz, S. (1998). Collective dynamics of small worlds. Nature, 393, 440–442.CrossRefGoogle Scholar
  45. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Sara Green
    • 1
    Email author
  • Maria Şerban
    • 1
  • Raphael Scholl
    • 2
  • Nicholaos Jones
    • 3
  • Ingo Brigandt
    • 4
  • William Bechtel
    • 5
  1. 1.University of CopenhagenCopenhagenDenmark
  2. 2.University of GenevaGenevaSwitzerland
  3. 3.University of Alabama in HuntsvilleHuntsvilleUSA
  4. 4.University of California, San DiegoLa JollaUSA
  5. 5.University of CaliforniaSan DiegoUSA

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