Understanding endogenously active mechanisms: A scientific and philosophical challenge

Original paper in Philosophy of Science

Abstract

Although noting the importance of organization in mechanisms, the new mechanistic philosophers of science have followed most biologists in focusing primarily on only the simplest mode of organization in which operations are envisaged as occurring sequentially. Increasingly, though, biologists are recognizing that the mechanisms they confront are non-sequential and the operations nonlinear. To understand how such mechanisms function through time, they are turning to computational models and tools of dynamical systems theory. Recent research on circadian rhythms addressing both intracellular mechanisms and the intercellular networks in which these mechanisms are synchronized illuminates this point. This and other recent research in biology shows that the new mechanistic philosophers of science must expand their account of mechanistic explanation to incorporate computational modeling, yielding dynamical mechanistic explanations. Developing such explanations, however, is a challenge for both the scientists and the philosophers as there are serious tensions between mechanistic and dynamical approaches to science, and there are important opportunities for philosophers of science to contribute to surmounting these tensions.

Keywords

New mechanistic philosophy of science Dynamical mechanistic explanation Computational modeling Circadian rhythms Dynamical systems theory Non-sequential organization 

References

  1. Abrahamsen, A., & Bechtel, W. (2011). From reactive to endogenously active dynamical conceptions of the brain. In T. Reydon & K. Plaisance (Eds.), Philosophy of behavioral biology. New York: Spinger.Google Scholar
  2. Aton, S. J., & Herzog, E. D. (2005). Come together, right…now: Synchronization of rhythms in a mammalian circadian clock. Neuron, 48(4), 531–534.CrossRefGoogle Scholar
  3. Bechtel, W. (2009). Generalization and discovery by assuming conserved mechanisms: Cross species research on circadian oscillators. Philosophy of Science, 76, 762–773.Google Scholar
  4. Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421–441.CrossRefGoogle Scholar
  5. Bechtel, W., & Abrahamsen, A. (2009). Decomposing, recomposing, and situating circadian mechanisms: Three tasks in developing mechanistic explanations. In H. Leitgeb & A. Hieke (Eds.), Reduction and elimination in philosophy of mind and philosophy of neuroscience (pp. 173–186). Frankfurt: Ontos Verlag.Google Scholar
  6. Bechtel, W., & Abrahamsen, A. (2010). Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science. Studies in History and Philosophy of Science Part A, 41(3), 321–333.CrossRefGoogle Scholar
  7. 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
  8. Boogerd, F., Bruggeman, F. J., Hofmeyr, J.-H., & Westerhoff, H. (Eds.). (2007). Systems biology: Philosophical perspectives. Amsterdam: Elsevier.Google Scholar
  9. Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.CrossRefGoogle Scholar
  10. Chemero, A. (2000). Anti-representationalism and the dynamical stance. Philosophy of Science, 67(4), 625–647.CrossRefGoogle Scholar
  11. Craver, C. F. (2007). Explaining the brain: What a science of the mind-brain could be. New York: Oxford University Press.Google Scholar
  12. Darden, L. (2006). Reasoning in biological discoveries: Essays on mechanisms, interfield relations, and anomaly resolution. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Felleman, D. J., & van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.CrossRefGoogle Scholar
  14. Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44, 50–71.CrossRefGoogle Scholar
  15. Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69, S342–S353.CrossRefGoogle Scholar
  16. Goldbeter, A. (1995). A model for circadian oscillations in the Drosophila Period protein (PER). Proceedings of the Royal Society of London. B: Biological Sciences, 261(1362), 319–324.CrossRefGoogle Scholar
  17. Gonze, D., Bernard, S., Waltermann, C., Kramer, A., & Herzel, H. (2005). Spontaneous synchronization of coupled circadian oscillators. Biophysical Journal, 89(1), 120–129.CrossRefGoogle Scholar
  18. Goodwin, B. C. (1965). Oscillatory behavior in enzymatic control processes. Advances in Enzyme Regulation, 3, 425–428.CrossRefGoogle Scholar
  19. Grebogi, C., Ott, E., & Yorke, J. A. (1987). Chaos, strange attractors, and fractal basin boundaries in nonlinear dynamics. Science, 238(4827), 632–638.CrossRefGoogle Scholar
  20. Hardin, P. E., Hall, J. C., & Rosbash, M. (1990). Feedback of the Drosophila period gene product on circadian cycling of its messenger RNA levels. Nature, 343(6258), 536–540.CrossRefGoogle Scholar
  21. Hempel, C. G. (1965). Aspects of scientific explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation and other essays in the philosophy of science (pp. 331–496). New York: Macmillan.Google Scholar
  22. Herzog, E. D., Aton, S. J., Numano, R., Sakaki, Y., & Tei, H. (2004). Temporal precision in the mammalian circadian system: A reliable clock from less reliable neurons. Journal of Biological Rhythms, 19(1), 35–46.CrossRefGoogle Scholar
  23. Kaplan, D. M., & Craver, C. (2011). The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective.Google Scholar
  24. Konopka, R. J., & Benzer, S. (1971). Clock mutants of Drosophila melanogaster. Proceedings of the National Academy of Sciences (USA), 89, 2112–2116.CrossRefGoogle Scholar
  25. Leloup, J.-C., & Goldbeter, A. (2000). Modeling the molecular regulatory mechanism of circadian rhythms in Drosophila. BioEssays, 22(1), 84–93.CrossRefGoogle Scholar
  26. Leloup, J.-C., & Goldbeter, A. (2004). Modeling the mammalian circadian clock: Sensitivity analysis and multiplicity of oscillatory mechanisms. Journal of Theoretical Biology, 230(4), 541–562.CrossRefGoogle Scholar
  27. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.CrossRefGoogle Scholar
  28. Noble, D. (2006). The music of life: Biology beyond the genome. Oxford: Oxford University Press.Google Scholar
  29. Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, N.J.: Princeton University Press.Google Scholar
  30. Silver, R., LeSauter, J., Tresco, P. A., & Lehman, M. N. (1996). A diffusible coupling signal from the transplanted suprachiasmatic nucleus controlling circadian locomotor rhythms. Nature, 382(6594), 810–813.CrossRefGoogle Scholar
  31. Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: MIT Press.Google Scholar
  32. Smolen, P., Hardin, P. E., Lo, B. S., Baxter, D. A., & Byrne, J. H. (2004). Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK. Biophysical Journal, 86(5), 2786–2802.CrossRefGoogle Scholar
  33. Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2(2), 145–162.CrossRefGoogle Scholar
  34. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.CrossRefGoogle Scholar
  35. To, T.-L., Henson, M. A., Herzog, E. D., & Doyle, F. J., III. (2007). A molecular model for intercellular synchronization in the mammalian circadian clock. Biophysical Journal, 92(11), 3792–3803.CrossRefGoogle Scholar
  36. van den Pol, A. N. (1980). The hypothalamic suprachiasmatic nucleus of rat: Intrinsic anatomy. The Journal of Comparative Neurology, 191(4), 661–702.CrossRefGoogle Scholar
  37. Vasalou, C., Herzog, E. D., & Henson, M. A. (2009). Small-world network models of intercellular coupling predict enhanced synchronization in the suprachiasmatic nucleus. Journal of Biological Rhythms, 24(3), 243–254.CrossRefGoogle Scholar
  38. Watts, D., & Strogratz, S. (1998). Collective dynamics of small worlds. Nature, 393(440–442).Google Scholar
  39. Weber, M. (2005). Philosophy of experimental biology. Cambridge: Cambridge University Press.Google Scholar
  40. Welsh, D. K., Engle, E. M. R. A., Richardson, G. S., & Dement, W. C. (1986). Precision of circadian wake and activity onset timing in the mouse. Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology, 158(6), 827–834.CrossRefGoogle Scholar
  41. Welsh, D. K., Logothetis, D. E., Meister, M., & Reppert, S. M. (1995). Individual neurons dissociated from rat suprachiasmatic nucleus express independently phased circadian firing rhythms. Neuron, 14(4), 697–706.CrossRefGoogle Scholar
  42. Wimsatt, W. C. (1976). Reductive explanation: A functional account. In R. S. Cohen, C. A. Hooker, A. C. Michalos, & J. van Evra (Eds.), PSA-1974 (pp. 671–710). Dordrecht: Reidel.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Philosophy, Center for Chronobiology, and Science Studies ProgramUniversity of CaliforniaLa JollaUSA

Personalised recommendations