Explanation and Organizing Principles in Systems Biology

Part of the History, Philosophy and Theory of the Life Sciences book series (HPTL, volume 11)

Abstract

While explanation in biology is a well-established topic in philosophy of science, since the rise of new mechanistic approaches little has been said about explanation in Systems Biology. In this contribution, we analyze whether contemporary conceptualizations of explanation fit with the scientific practice of systems approaches in molecular and cell biology. We discuss how current views on mechanistic explanation can be applied to the use of mathematical models in system biology. To this end it is important to distinguish different kinds of models: While some of them merely save the phenomena, not being explanatory, others make mechanistic claims and can be embedded in the framework of mechanistic explanations. We will make a conceptual distinction and discuss the roles different kinds of models have in Systems Biology. In our view, the current mechanistic framework is not sufficient to capture all types of explanations occurring in Systems Biology. A multicellular system is not merely defined by its structural components, including genes, proteins and their interconnections, but has a functional organization, that is the system’s behavior, which emerges from its structural organization. Such emergent properties cannot be understood or explained by merely describing the mechanisms of the underlying molecular and cellular processes. We argue that organizing principles in Systems Biology provides a way of explaining such high-level system properties. With the help of a case study we show that organizing principles explain system-properties complementary to mechanistic explanations. They cannot replace them but they also cannot be reduced to them.

Keywords

Systems biology Models in science Mechanistic models Mechanistic explanation Organizing principles Robustness 

References

  1. Alon, U., Surette, M. G., Barkai, N., & Leibler, S. (1999). Robustness in bacterial chemotaxis. Nature, 397, 168–171.CrossRefGoogle Scholar
  2. Baker, A. (2015). Mathematical explanation in biology. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 229–247). Dordrecht: Springer.Google Scholar
  3. Barkai, N., & Leibler, S. (1997). Robustness in simple biochemical networks. Nature, 387, 913–917.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 mechanistic alternative. Studies in the History and Philosophy of Biology and the Biomedical Sciences, 36, 421–441.CrossRefGoogle Scholar
  6. Bogen, J., & Woodward, J. (1988). Saving the phenomena. The Philosophical Review, 97, 303–352.CrossRefGoogle Scholar
  7. Carlson, J. M., & Doyle, J. (2002). Complexity and robustness. Proceedings of the National Academy of Sciences, 99, 2538–2545.CrossRefGoogle Scholar
  8. Cartwright, N., & Le Poidevin, R. (1991). Fables and models. Proceedings of the Aristotelian Society, 65, 55–82.Google Scholar
  9. Craver, C. (2006). When mechanistic models explain. Synthese, 153, 355–376.CrossRefGoogle Scholar
  10. Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.CrossRefGoogle Scholar
  11. Cummins, R. C. (2000). ‘How does it work’ versus ‘what are the laws?’: Two conceptions of psychological explanation. In F. Keil & R. A. Wilson (Eds.), Explanation and cognition (pp. 117–145). Cambridge, MA: MIT Press.Google Scholar
  12. Friedman, M. (1974). Explanation and scientific understanding. Journal of Philosophy, 71, 5–19.CrossRefGoogle Scholar
  13. Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69, S342–S353.CrossRefGoogle Scholar
  14. Gunawardena, J. (2010). Biological systems theory. Science, 328, 581.CrossRefGoogle Scholar
  15. Issad, T., & Malaterre, C. (2015). Are dynamic mechanistic explanations still mechanistic? In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 265–292). Dordrecht: Springer.Google Scholar
  16. Kitano, H. (2004). Biological robustness. Nature Reviews Genetics, 5, 826–837.CrossRefGoogle Scholar
  17. Kitcher, P. (1989). Explanatory unification and the causal structure of the world. In P. Kitcher & W. Salmon (Eds.), Scientific explanation (pp. 410–505). Minneapolis: University of Minnesota Press.Google Scholar
  18. Lipton, P. (2004). Inference to the best explanation (2nd ed.). London: Routledge.Google Scholar
  19. Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.CrossRefGoogle Scholar
  20. Mesarovic, M. D., Sreenath, S. N., & Keene, J. D. (2004). Searching for organizing principle: Understanding in systems biology. Systems Biology, 1, 19–27.CrossRefGoogle Scholar
  21. Morgan, M. (1999). Models as mediating instruments. In M. Morgan & M. Morrison (Eds.), Models as mediators: Perspectives on natural and social science (pp. 10–37). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  22. Novác, B., & Tyson, J. J. (2008). Design principles of biochemical oscillators. Nature Reviews Molecular Cell Biology, 9, 981–991.CrossRefGoogle Scholar
  23. Schwob, E., & Nasmyth, K. (1993). CLB5 and CLB6, a new pair of B cyclins involved in DNA replication in Saccharomyces cerevisiae. Genes and Development, 7, 1160–1175.CrossRefGoogle Scholar
  24. Shinar, G., & Feinberg, M. (2011). Design principles for robust biochemical reaction networks: What works, what cannot work, and what might almost work. Mathematical Biosciences, 231(2011), 39–48.CrossRefGoogle Scholar
  25. Shinar, G., Alon, U., & Feinberg, M. (2009). Sensitivity and robustness in chemical reaction networks. SIAM Journal of Applied Mathematics, 69, 977–998.CrossRefGoogle Scholar
  26. Stelling, J., Sauer, W., Szallasi, Z., Doyle, F. J., & Doyle, J. (2004). Robustness of cellular functions. Cell, 118, 675–685.CrossRefGoogle Scholar
  27. Weisberg, M. (2007). Who is a modeler? British Journal for the Philosophy of Science, 58, 207–233.CrossRefGoogle Scholar
  28. Wolkenhauer, O., et al. (2009). Advancing systems biology for medical applications. IET Systems Biology, 3, 131–136.CrossRefGoogle Scholar
  29. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Systems Biology and Bioinformatics GroupUniversity of RostockRostockGermany
  2. 2.Department of Systems Biology & BioinformaticsUniversity of RostockRostockGermany
  3. 3.Stellenbosch Institute for Advanced StudyWallenberg Research Centre at Stellenbosch UniversityStellenboschSouth Africa

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