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4 - What Is the Nature of Theories and Models in Biology?

Published online by Cambridge University Press:  04 September 2020

By
Emily C. Parke  and
Anya Plutynski
Edited by
Kostas Kampourakis  and
Tobias Uller
Kostas Kampourakis
Affiliation:
Université de Genève
Tobias Uller
Affiliation:
Lunds Universitet, Sweden
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Summary

Biologists try to understand the living world at large by zooming in on tractable portions of it and by constructing and studying models. If you want to study long-term evolution in real time, you can follow finch populations in their natural habitats in the Galápagos (Grant & Grant 1993) or you can evolve microbial models in flasks in a laboratory (Lenski et al. 1991). If you want to understand how multicellularity first emerged in eukaryotes, you cannot go back in time to examine that event directly. Instead, you can construct and study a variety of models: for example, agent-based computer simulations, model organisms such as yeast or volvox, or model-based phylogenetic reconstructions (Bonner et al. 2016).

Type
Chapter
Information
Philosophy of Science for Biologists , pp. 55 - 78
Publisher: Cambridge University Press
Print publication year: 2020

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References

Agutter, P. S. & Wheatley, D. N. (2004). Metabolic Scaling: Consensus or Controversy? Theoretical Biology and Medical Modelling 1(13).Google Scholar
Ankeny, R. A. & Leonelli, S. (2011). What’s So Special about Model Organisms? Studies in History and Philosophy of Science Part A 42(2): 313–323.Google Scholar
Axelrod, R. (1984). The Evolution of Cooperation. New York: Basic Books.Google Scholar
Beatty, J. (1997). Why Do Biologists Argue like They Do? Philosophy of Science S64: 231–242.Google Scholar
Beatty, J. (2006). Replaying Life’s Tape. The Journal of Philosophy 103(7): 336–362.Google Scholar
Bissell, J. (2007). The Moniac: A Hydromechanical Analog Computer of the 1950s. IEEE Control Systems Magazine 27(1): 69–74.Google Scholar
Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008). Historical Contingency and the Evolution of a Key Innovation in an Experimental Population of Escherichia Coli. Proceedings of the National Academy of Sciences of the United States of America 105(23): 7899–7906.Google Scholar
Bonner, J. T. (2016). Multicellularity: Origins and Evolution. Cambridge, MA: MIT Press.Google Scholar
Brodland, G. W. (2015). How Computational Models Can Help Unlock Biological Systems. Seminars in Cell & Developmental Biology 47–48: 62–73.Google Scholar
Cartwright, N. (1983). How the Laws of Physics Lie. Oxford: Clarendon Press.Google Scholar
Currie, A. & Levy, A. (2019). Why Experiments Matter. Inquiry 62(9–10): 1066–1090.Google Scholar
Dobzhansky, T. (1936). Studies on Hybrid Sterility. II. Localization of Sterility Factors in Drosophila Pseudoobscura Hybrids. Genetics 21(2): 113–135.Google Scholar
Downes, S. M. (1992). The Importance of Models in Theorizing: A Deflationary Semantic View. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992(1): 142–153.Google Scholar
Elena, S. F., Cooper, V. S. & Lenski, R. E. (1996). Punctuated Evolution Caused by Selection of Rare Beneficial Mutations. Science 272(5269): 1802.Google Scholar
Fenchel, T. (1974). Intrinsic Rate of Natural Increase: The Relationship with Body Size. Oecologia 14(4): 317–326.Google Scholar
Frigg, R. (2010). Fiction and Scientific Representation. In Frigg, R. & Hunter, M. (eds.), Beyond Mimesis and Nominalism: Representation in Art and Science, pp. 97–138. Dordrecht: Springer.Google Scholar
Frigg, R. & Hartmann, S. (2012). Models in Science. In Zalta, E. (ed.), The Stanford Encyclopedia of Philosophy (Summer 2018 ed.). https://plato.stanford.edu/archives/sum2018/entries/models-science/Google Scholar
Giere, R. N. (1988). Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.Google Scholar
Giere, R. N. (1999). Science without Laws. Chicago: University of Chicago Press.Google Scholar
Gillespie, J. H. (2000). Genetic Drift in an Infinite Population: The Pseudohitchhiking Model. Genetics 155(2): 909–919.Google Scholar
Godfrey-Smith, P. (2006). The Strategy of Model-Based Science. Biology and Philosophy 21(5): 725–740.Google Scholar
Godfrey-Smith, P. (2009). Modes and Fictions in Science. Philosophical Studies 143(1): 101–116.Google Scholar
Goel, N., Campbell, R. D., Gordon, R., Rosen, R., Martinez, H., & Yčas, M. (1970). Self-Sorting of Isotropic Cells. Journal of Theoretical Biology 28(3): 423–468.Google Scholar
Gould, S. J. & Eldredge, N. (1977). Punctuated Equilibria: The Tempo and Mode of Evolution Revisited. Paleobiology 3(2): 115–151.Google Scholar
Grant, B. R. & Grant, P. R. (1993). Evolution of Darwin’s Finches Caused by a Rare Climatic Event. Proceedings of the Royal Society of London B 251(1331): 111–117.Google Scholar
Guala, F. (2002). Models, Simulations and Experiments. In Magnani, L. & Nersessian, N. J. (eds.), Model-Based Reasoning: Science, Technology, Values, pp. 59–74. Dordrecht: Kluwer.Google Scholar
Kingsland, S. E. (1995). Modeling Nature. Chicago: University of Chicago Press.Google Scholar
Kingsland, S. E. (2005). The Evolution of American Ecology, 1890–2000. Baltimore: John Hopkins University Press.Google Scholar
Lenski, R. E. (2011). Evolution in Action: A 50,000-Generation Salute to Charles Darwin. Microbe 6(1): 30–33.Google Scholar
Lenski, R. E., Ofria, C., Pennock, R. T., & Adami, C. (2003). The Evolutionary Origin of Complex Features. Nature 423: 139–144.Google Scholar
Lenski, R. E., Rose, M. R., Simpson, S. C., & Tadler, S. C. (1991). Long-Term Experimental Evolution in Escherichia Coli. I. Adaptation and Divergence during 2,000 Generations. The American Naturalist 138(6): 1315–1341.Google Scholar
Levins, R. (1966). The Strategy of Model Building in Population Genetics. American Scientist 54(4): 421–431.Google Scholar
Levy, A. (2018). Idealization and Abstraction: Refining the Distinction. Synthese 1–18.Google Scholar
Levy, A. & Currie, C. (2014). Model Organisms Are Not (Theoretical) Models. The British Journal for the Philosophy of Science 66(2): 327–348.Google Scholar
Lloyd, E. A. (1994). The Structure and Confirmation of Evolutionary Theory. Princeton, NJ: Princeton University Press.Google Scholar
Lloyd, E. A. (2015). Model Robustness as a Confirmatory Virtue: The Case of Climate Science. Studies in History and Philosophy of Science Part A 49: 58–68.Google Scholar
Matthewson, J. & Weisberg, M. (2009). The Structure of Tradeoffs in Model Building. Synthese 170(1): 169–190.Google Scholar
McIntosh, R. P. (1986). The Background of Ecology: Concept and Theory. Cambridge: Cambridge University Press.Google Scholar
Morgan, M. S. (2005). Experiments versus Models: New Phenomena, Inference and Surprise. Journal of Economic Methodology 12(2): 317–329.Google Scholar
Morgan, M. S. & Morrison, M. (eds.) (1999). Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press.Google Scholar
Muller, H. J. (1942). Isolating Mechanisms, Evolution, and Temperature. Biology Symposium 6:71–125.Google Scholar
O’Malley, M. A. & Parke, E. C. (2018). Microbes, Mathematics, and Models. Studies in History and Philosophy of Science Part A 72: 1–10.Google Scholar
Orzack, S. H. & Sober, E. (1993). A Critical Assessment of Levins's The Strategy of Model Building in Population Biology (1966). The Quarterly Review of Biology 68(4): 533–546.Google Scholar
Parke, E. C. (2014). Experiments, Simulations, and Epistemic Privilege. Philosophy of Science 81(4): 516–536.Google Scholar
Parker, W. S. (2009). Does Matter Really Matter? Computer Simulations, Experiments, and Materiality. Synthese 169(3): 483–496.Google Scholar
Potochnik, A. (2017). Idealization and the Aims of Science. Chicago: University of Chicago Press.Google Scholar
Provine, W. B. (2001). The Origins of Theoretical Population Genetics: With a New Afterword. Chicago: University of Chicago Press.Google Scholar
Roush, S. (2018). The Epistemic Superiority of Experiment to Simulation. Synthese 195(11): 4883–4906.Google Scholar
Savage, V. M., Allen, A. P., Brown, J. H., Gillooly, J. F., Herman, A. B., Woodruff, W. B., & West, G. B. (2007). Scaling of Number, Size, and Metabolic Rate of Cells with Body Size in Mammals. Proceedings of the National Academy of Sciences 104(11): 4718–4723.Google Scholar
Servedio, M. R., Brandvain, Y., Dhole, S., Fitzpatrick, C. L., Goldberg, E. E., Stern, C. A., Cleve, J. V., & Yeh, D. J. (2014). Not Just a Theory – The Utility of Mathematical Models in Evolutionary Biology. PLoS Biology 12(12): e1002017.Google Scholar
Sniegowski, P., Gerrish, P. J., & Lenski, R. E. (1997). Evolution of High Mutation Rates in Experimental Populations of E. Coli. Nature 387(6634): 703–705.Google Scholar
Sprouffske, K., Merlo, L. M., Gerrish, P. J., Maley, C. C., & Sniegowski, P. D. (2012). Cancer in Light of Experimental Evolution. Current Biology 22(17): R762–R771.Google Scholar
Suppe, F. (1989). The Semantic Conception of Theories and Scientific Realism. Chicago: University of Illinois Press.Google Scholar
Travisano, M., Mongold, J. A., Bennett, A. F., & Lenski, R. E. (1995). Experimental Tests of the Roles of Adaptation, Chance, and History in Evolution. Science 267(5194): 87–90.CrossRefGoogle ScholarPubMed
van Fraassen, B. (1980). The Scientific Image. Oxford: Oxford University Press.Google Scholar
Weisberg, M. (2013). Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.CrossRefGoogle Scholar

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