Abstract
Marcello Barbieri has presented code biology as an alternative to the Peircean approach to biosemiotics. Some critics questioned the viability of code biology on grounds that Barbieri’s conception of science is limited. It has been argued that code biology’s mechanistic tendency is the cause of the allegedly limited conception of science. In this paper, I evaluate the scientific viability of the code model from the perspective of scientific realism in the philosophy of science. To be more precise, I draw on resources of the mechanistic view in philosophy (aka New Mechanistic Philosophy) to argue that far from harbouring a limited conception of science, code biology could indeed improve the scientific prospects of biosemiotics. I show that the mechanistic and model-based tendency of the code model enhances its vigour as a full-blooded scientific approach to the study of meaning in living systems. To consolidate my claim, I draw on some recent debates in the mechanistic philosophy to argue that even relational approaches to understanding the meaning in living systems—such as an approach that is defended by Vega in a recent paper—could be underpinned by mechanistic processes at a foundational level.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
According to Barbieri, “a code is: a small set of arbitrary rules selected from a potentially unlimited number in order to ensure a specific correspondence between two independent worlds” (Barbieri 2014: 4).
I thank one of the reviewers of this journal for remarking this point.
I thank one of the reviewers of this journal for bringing this point into my attention.
Obviously, the point that Nicholson does not include being organized or structured as a condition of mechanisms, even if true, does not count as an argument for Vega’s denial of Bechtel’s argument.
I thank one of the reviewers of this journal for impressing the importance of this point upon me.
For example, Levin and Martyniuk (2018) explain that morphogenesis or the emergence of large scale anatomical properties cannot be explicated on the basis of pure geometrical structures which lack temporal dimensionality. Morphogenesis is based on dynamical pattern-homeostatic processes which presumes the mechanisms of bioelectrical signalling in terms of temporal changes in such a pattern. A similar situation has been described in Ma et al.’s (2018) classification of space-time maps that compile sampled protrusion and retraction velocities. The space-time maps can be used identify distinct cell morphodynamic states and specifying functional links to underlying cytoskeleton dynamics.
References
Atkin, A. (2010). Peirce’s theory of signs. In The Stanford encyclopedia of philosophy (summer 2013 edition). Stanford University. https://plato.stanford.edu/entries/peirce-semiotics/. Accessed 3 March 2019.
Barbieri, M. (2008). Biosemiotics: a new understanding of life. Die Naturwissenschaften, 95(7), 577–599.
Barbieri, M. (2009). Three types of semiosis. Biosemiotics, 2(1), 19–30. https://doi.org/10.1007/s12304-008-9038-9.
Barbieri, M. (2011). A mechanistic model of meaning. Biosemiotics, 4(1), 1–4. https://doi.org/10.1007/s12304-010-9103-z.
Barbieri, M. (2014). From biosemiotics to code biology. Biological Theory, 9(2), 239–249. https://doi.org/10.1007/s13752-013-0155-6.
Barbieri, M. (2015). Code biology. Cham: Springer International Publishing.
Bechtel, W. (2017). Analysing wetwork models to make discoveries about biological mechanisms. The British Journal for the Philosophy of Science, aaxx(051), 21–25. https://doi.org/10.1093/bjps/axx051.
Bechtel, W., & Abrahamsen, A. (2005). Explanation: a mechanist alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441. https://doi.org/10.1016/j.shpsc.2005.03.010.
Bechtel, W., & Richardson, R. C. (2010). Discovering complexity : Decomposition and localization as strategies in scientific research. Cambridge: MIT Press https://mitpress.mit.edu/books/discovering-complexity.
Craver, C. F. (2007). Explaining the brain : Mechanisms and the mosaic unity of neuroscience. Oxford: Clarendon Press.
Craver, C. F. (2014). The ontic account of scientific explanation. In Explanation in the special sciences (pp. 27–52). Dordrecht: Springer Netherlands.
Craver, C. F. (2016). The explanatory power of network models. Philosophy of Science, 83(5), 698–709. https://doi.org/10.1086/687856.
Craver, C. F., & Povich, M. (2017). The directionality of distinctively mathematical explanations. Studies in History and Philosophy of Science Part A, 63, 31–38. https://doi.org/10.1016/J.SHPSA.2017.04.005.
Gabius, H.-J. (2018). The sugar code: why glycans are so important. BioSystems, 164, 102–111. https://doi.org/10.1016/j.biosystems.2017.07.003.
Giere, R. N. (1999). Using models to represent reality. In Model-mased reasoning in scientific discovery (pp. 41–57). Boston: Springer US.
Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752. https://doi.org/10.1086/425063.
Glennan, S. (2017). The new mechanical philosophy (Vol. 1). Oxford: Oxford University Press.
Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21(5), 725–740. https://doi.org/10.1007/s10539-006-9054-6.
Godfrey-Smith, P. (2009). Models and fictions in science. Philosophical Studies, 143(1), 101–116. https://doi.org/10.1007/s11098-008-9313-2.
Hempel, C. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: Free Press.
Hoffmeyer, J. (2008). Biosemiotics. An examination into the signs of life and the life of signs. Scranton: University of Scranton Press.
Hofmeyr, J.-H. S. (2018). Causation, constructors and codes. BioSystems, 164, 121–127. https://doi.org/10.1016/j.biosystems.2017.09.008.
Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177(2), 213–245. https://doi.org/10.1007/s11229-010-9842-z.
Huneman, P. (2018). Outlines of a theory of structural explanations. Philosophical Studies, 175(3), 665–702. https://doi.org/10.1007/s11098-017-0887-4.
Kubyshkin, V., Acevedo-Rocha, C. G., & Budisa, N. (2018). On universal coding events in protein biogenesis. BioSystems, 164, 16–25. https://doi.org/10.1016/j.biosystems.2017.10.004.
Levin, M., & Martyniuk, C. J. (2018). The bioelectric code: an ancient computational medium for dynamic control of growth and form. BioSystems, 164, 76–93. https://doi.org/10.1016/j.biosystems.2017.08.009.
Ma, X., Dagliyan, O., Hahn, K. M., & Danuser, G. (2018). Profiling cellular morphodynamics by spatiotemporal spectrum decomposition. PLoS Computational Biology, 14(8), e1006321. https://doi.org/10.1371/journal.pcbi.1006321.
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25. https://doi.org/10.1086/392759.
Nicholson, D. J. (2012). The concept of mechanism in biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 152–163. https://doi.org/10.1016/j.shpsc.2011.05.014.
Nicholson, D. J. (2013). Organisms≠Machines. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44(4), 669–678. https://doi.org/10.1016/j.shpsc.2013.05.014.
Psillos, S. (2011). Choosing the realist framework. In Synthese. Netherland: Springer. https://doi.org/10.2307/41477558.
Rathkopf, C. (2018). Network representation and complex systems. Synthese, 195(1), 55–78. https://doi.org/10.1007/s11229-015-0726-0.
Rosen, R. (1991). Life itself : a comprehensive inquiry into the nature, origin, and fabrication of life.. New York: Columbia University Press.
Rosen, R. (2012). Anticipatory systems : Philosophical, mathematical, and methodological foundations. New York: Springer.
Sebeok, T. A. (1990). Essays in zoosemiotics. Toronto: Toronto Semiotic Circle.
Sebeok, T. A. (2001). Biosemiotics: its roots, proliferation, and prospects. Semiotica, 2001(134). https://doi.org/10.1515/semi.2001.014.
Short, T. L. (2007). Peirce’s theory of signs. Cambridge: Cambridge University Press.
Uexküll, J. von. (2010). A foray into the worlds of animals and humans : with A theory of meaning. (trans. Joseph D. O’Neil, Ed.). University of Minnesota Press.
Vega, F. (2018). A critique of Barbieri’s code biology through Rosen’s relational biology: Reconciling Barbieri’s biosemiotics with Peircean biosemiotics. Biological Theory, 13, 1–19. https://doi.org/10.1007/s13752-018-0302-1.
Weisberg, M. (2013). Simulation and similarity using models to understand the world. Oxford: Oxford University Press.
Acknowledgments
I have to thank Marcello Barbieri and Stephen Cowley for their insightful comments. I also benefited a lot from the comments of three anonymous referees of Biosemiotics as well as the journal’s editors. All of these debts are gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Beni, M.D. New Mechanistic Philosophy and the Scientific Prospects of Code Biology. Biosemiotics 12, 197–211 (2019). https://doi.org/10.1007/s12304-019-09360-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12304-019-09360-0