Abstract
Contemporary philosophy of science has seen a growing trend towards a focus on scientific practice over the epistemic outputs that such practices produce. This practice-oriented approach has yielded a clearer understanding of how reductive research strategies play a central role in contemporary scientific inquiry. In parallel, a growing body of work has sought to explore the role of non-reductive, or systems-level, research strategies. As a result, the relationship between reductive and non-reductive scientific practices is becoming of increased importance. In this paper, I provide a framework within which research strategies can be compared. I argue that no strategy is reductive or non-reductive simpliciter, rather strategies are more, or are less, reductive than one another according to a frame of reference. That frame of reference is provided by a continuum of possible ways in which the target system might be conceptualised. I illustrate the utility of the framework by deploying it to analyse a recent debate in cancer research. When set within the framework, a prominent reductive strategy—the somatic mutation theory—and a prominent non-reductive strategy—the tissue organisational field theory—do not stand opposed to one another. Rather, they serve as boundary markers to chart the territory of approaches to carcinogenesis within which most strategies in the field fall.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
I’d like to thank Anna Kocsis, Maria Kronfeldner, Michele Luchetti, Garrett Mindt, and Lena Zuchowski, for criticisms, discussions, and support at various stages of developing the ideas in this paper. I’d also like to thank two anonymous reviewers for their very helpful and constructive comments.
The language of these debates sometimes masks an underlying standoff. For example, Kaplan and Bechtel’s (2011) strategy to dissolve a standoff between mechanistic and dynamical approaches is to argue that there is no competition between them because the former is in the business of providing explanations whereas the latter is not (2011, p. 443; see also Kaplan 2011, p. 367). To me, this seems less like the dissolution of a standoff and more like the development of an epistemic standoff—reductive (in the mechanistic sense) approaches explain whereas non-reductive (at least in dynamic systems modelling) approaches do not. My aim is to move beyond this kind of epistemic standoff by remaining neutral on the explanatory potential of different approaches and instead to analyse their relationship to one another in terms of key aspects of system conceptualisation.
To be clear I am not suggesting that it is impossible for different research strategies to reach an insurmountable standoff. Rather, my argument is that the framework I develop here will show that how reduction can be understood as a comparative relation rather than being absolute. In the example I discuss here, apparently competing theories will be shown to be boundary markers for a field of inquiry rather than competitors. I am confident that this framework will be generalisable beyond the example I use here. However, that expansion requires further empirical cases and their specific details, opening a project for future development.
For example, often practical decisions or practical obstacles will strongly affect system conceptualisation (theoretical assumptions). Simplifying assumptions are often required given limitations of experimental tools available or a lack of knowledge about the system at a given stage of inquiry e.g., in the search for an appropriate unit of intervention for the system. Assumptions such as studying parts in isolation from their environment context (Kaiser 2015, pp. 225–229), or homogenising or fixing environmental factors in space and time (Wimsatt 2007, pp. 347–352) sometimes (but not always) follow from these sorts of practical considerations. In this paper, my analysis is focused on the theoretical assumptions that result in system conceptualisation as a starting point for a framework of analysis. Should this analysis be convincing, then important specificity will surely be added with a thorough treatment of how and why those given theoretical assumptions are adopted, however that work remains for later development.
For a detailed but clear and accessible explication of nonlinearity see Favela (2015, pp. 45–47).
I should be clear that these usages of interaction dominance and component dominance are inspired by, but not necessarily the same as, their usage in dynamic systems theory. However, I think my use of them as container terms for the dynamics of a system is certainly still faithful to what they represent in dynamic systems theory.
As testament to this claim, even for the Cancer Genome Atlas project almost all 33 types of cancer investigated are classified according to the tissue/organ in which they present, and then further sub-classified at the molecular level. Squamous Cell Carcinoma is an exception, although the project focused only on squamous cells collected from the mouth, nose, and throat. Full list is available here: https://cancergenome.nih.gov/cancersselected.
On a cautionary note, this issue remains contested. On the one hand intra-tumour heterogeneity (ITH) is a major area of research, with some claiming that genetic (as well as epigenetic and phenotypic) ITH is now widely recognised across many major tumour types; putting some pressure on Vaux’s argument (Marusyk et al. 2012; Gay et al. 2016). On the other hand, novel treatments—such as BRAF inhibitors for the treatment of metastatic melanoma—and detection methods have been developed that exploit the strong correlation between specific mutations and certain tumour types (Hodis 2012; Bruno et al. 2016).
References
Andersen, H. (2017). Reduction in the biomedical sciences. In M. Solomon, J. Simon, & H. Kincaid (Eds.), Routledge companion to philosophy of medicine. New York: Routledge.
Ayala, F. J. (1974). Introduction. In F. J. Ayala & T. Dobzhansky (Eds.), Studies in the philosophy of biology: Reduction and related problems (pp. 7–16). Berkeley: University of California Press.
Baker, S. G. (2009). Improving the biomarker pipeline to develop and evaluate cancer screening tests. Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djp186.
Baker, S. G. (2015). A cancer theory kerfuffle can lead to new lines of research. Journal of the National Cancer Institute, 107, 1–8. https://doi.org/10.1093/jnci/dju405.
Bechtel, W. (2015). Can mechanistic explanation be reconciled with scale-free constitution and dynamics? Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 53, 84–93.
Bechtel, W., & Richardson, R. C. (2010). Discovering complexity: Decomposition and localization as strategies in scientific research. Cambridge, Mass: MIT Press.
Bedessem, B., & Ruphy, S. (2015). SMT or TOFT? How the two main theories of carcinogenesis are made (artificially) incompatible. Acta Biotheoretica, 63, 257–267. https://doi.org/10.1007/s10441-015-9252-1.
Bertolaso, M. (2009). Towards an integrated view of the neoplastic phenomena in cancer research. History and Philosophy of the Life Sciences, 31, 79–97.
Bertolaso, M. (2011). Hierarchies and causal relationships in interpretative models of the neoplastic process. History and Philosophy of the Life Sciences, 33, 515–535.
Bissell, M. J., & Hines, W. C. (2011). Why don’t we get more cancer? A proposed role of the microenvironment in restraining cancer progression. Nature Medicine, 17, 320–329. https://doi.org/10.1038/nm.2328.
Brigandt, I. (2010). Beyond reduction and pluralism: Toward an epistemology of explanatory integration in biology. Erkenntnis, 73, 295–311. https://doi.org/10.1007/s10670-010-9233-3.
Brigandt, I. (2013). Systems biology and the integration of mechanistic explanation and mathematical explanation. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 477–492. https://doi.org/10.1016/j.shpsc.2013.06.002.
Brigandt, I., & Love, A. (2017). Reductionism in biology. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Stanford: Metaphysics Research Lab, Stanford University.
Bruno, W., et al. (2016). Heterogeneity and frequency of BRAF mutations in primary melanoma: Comparison between molecular methods and immunohistochemistry. Oncotarget, 8, 8069–8082. https://doi.org/10.18632/oncotarget.14094.
Chang, H. (2015). Reductionism and the relation between chemistry and physics. In T. Arabatzis, J. Renn, & A. Simões (Eds.), Relocating the history of science: Essays in honor of Kostas Gavroglu (pp. 193–209). Cham: Springer.
Craver, C. F. (2006). When mechanistic models explain. Synthese, 153, 355–376.
Craver, C. F., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. Chicago: The University of Chicago Press.
Darden, L. (2005). Relations among fields: Mendelian, cytological and molecular mechanisms. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 349–371. https://doi.org/10.1016/j.shpsc.2005.03.007.
Darden, L., & Maull, N. (1977). Interfield theories. Philosophy of Science, 44, 43–64. https://doi.org/10.1086/288723.
Favela, L. H. (2015). Understanding cognition via complexity science. Electronic Ph.D. Dissertation, University of Cincinnati.
Favela, L. H., & Martin, J. (2017). Cognition and dynamical cognitive science. Minds and Machines, 27, 331–355.
Fodor, J. (1974). Special sciences (or: the disunity of science as a working hypothesis). Synthese, 48, 97–115.
Gay, L., Baker, A.-M., & Graham, T.A. (2016). Tumour cell heterogeneity. F1000Research, 5. https://doi.org/10.12688/f1000research.7210.1.
Green, S. (2015). Revisiting generality in biology: Systems biology and the quest for design principles. Biology & Philosophy, 30, 629–652. https://doi.org/10.1007/s10539-015-9496-9.
Greenman, C., et al. (2007). Patterns of somatic mutation in human cancer genomes. Nature, 446, 153–158. https://doi.org/10.1038/nature05610.
Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100, 57–70.
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144, 646–674. https://doi.org/10.1016/j.cell.2011.02.013.
Hodis, E., et al. (2012). A landscape of driver mutations in melanoma. Cell, 150, 251–263. https://doi.org/10.1016/j.cell.2012.06.024.
Holden, J. G., Van Orden, G. C., & Turvey, M. T. (2009). Dispersion of response times reveals cognitive dynamics. Psychological Review, 116, 318.
Hull, D. (1972). Reduction in genetics: Biology or philosophy? Philosophy of Science, 39, 491–499.
Issad, T., & Malaterre, C. (2015). Are dynamic mechanistic explanations still explanations? 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.
Kaiser, M. I. (2011). The limits of reductionism in the life sciences. History and Philosophy of the Life Sciences, 33, 453–476.
Kaiser, M. I. (2015). Reductive explanation in the biological sciences. Cham: Springer.
Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183, 339–373.
Kaplan, D. M., & Bechtel, W. (2011). Dynamical models: An alternative or complement to mechanistic explanations? Topics in Cognitive Science, 3, 438–444.
Kaplan, D. M., & Craver, C. F. (2011). The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective. Philosophy of Science, 78, 601–627.
Kostić, D. (2016). The topological realization. Synthese, 1–20. https://doi.org/10.1007/s11229-016-1248-0.
Kuorikoski, J., & Ylikoski, P. (2013). How organization explains. In V. Karakostas & D. Dieks (Eds.), EPSA11 Perspectives and Foundational Problems in Philosophy of Science, The European Philosophy of Science Association Proceedings (pp. 69–80). Springer International Publishing. https://doi.org/10.1007/978-3-319-01306-0_6.
Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system? European Journal for Philosophy of Science, 3, 33–67. https://doi.org/10.1007/s13194-012-0056-8.
Laudan, L. (1977). Progress and its problems: Toward a theory of scientific growth. Berkeley: University of California Press.
Love, A. C. (2008). Explaining evolutionary innovations and novelties: Criteria of explanatory adequacy and epistemological prerequisites. Philosophy of Science, 75, 874–886. https://doi.org/10.1086/594531.
MacLeod, M., & Nersessian, N. J. (2015). Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 49, 1–11. https://doi.org/10.1016/j.shpsc.2014.10.004.
Maffini, M. V., Soto, A. M., Calabro, J. M., Ucci, A. A., & Sonnenschein, C. (2004). The stroma as a crucial target in rat mammary gland carcinogenesis. Journal of Cell Science, 117, 1495–1502. https://doi.org/10.1242/jcs.01000.
Mally, A., & Chipman, J. K. (2002). Non-genotoxic carcinogens: Early effects on gap junctions, cell proliferation and apoptosis in the rat. Toxicology, 180, 233–248. https://doi.org/10.1016/S0300-483X(02)00393-1.
Marusyk, A., Almendro, V., & Polyak, K. (2012). Intra-tumour heterogeneity: A looking glass for cancer? Nature Reviews Cancer, 12, 323–334. https://doi.org/10.1038/nrc3261.
Mitchell, S. D. (2009). Unsimple truths science, complexity, and policy. Chicago: University of Chicago Press.
Morange, M. (2007). The field of cancer research: An indicator of present transformations in biology. Oncogene, 26, 7607–7610. https://doi.org/10.1038/sj.onc.1210583.
Nagel, E. (1979). The structure of science: Problems in the logic of explanation (2nd ed.). Indianapolis: Hackett Publishing.
Needham, J. (1931). Chemical embryology. Cambridge: Cambridge University Press.
Oh, E.-Y., et al. (2015). Extensive rewiring of epithelial-stromal co-expression networks in breast cancer. Genome Biology, 16, 128. https://doi.org/10.1186/s13059-015-0675-4.
O’Malley, M. A., et al. (2014). Multilevel research strategies and biological systems. Philosophy of Science, 81, 811–828. https://doi.org/10.1086/677889.
Plutynski, A. (2013). Cancer and the goals of integration. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 466–476. https://doi.org/10.1016/j.shpsc.2013.03.019.
Rosenfeld, S. (2013). Are the somatic mutation and tissue organization field theories of carcinogenesis incompatible? Cancer Informatics, 2013, 221–229. https://doi.org/10.4137/CIN.S13013.
Rückert, F., Grützmann, R., & Pilarsky, C. (2012). Feedback within the inter-cellular communication and tumorigenesis in carcinomas. PLoS ONE, 7, e36719. https://doi.org/10.1371/journal.pone.0036719.
Sarkar, S. (1992). Models of reduction and categories of reductionism. Synthese, 91, 167–194. https://doi.org/10.1007/BF00413566.
Schaffner, K. F. (1967). Approaches to reduction. Philosophy of Science, 34, 137–147.
Silberstein, M., & Chemero, A. (2013). Constraints on localization and decomposition as explanatory strategies in the biological sciences. Philosophy of Science, 80, 958–970.
Simon, H. A. (1996). The sciences of the artificial. Cambridge, Mass: MIT Press.
Sonnenschein, C., & Soto, A. (2008). Theories of carcinogenesis: An emerging perspective. Seminars in Cancer Biology, 18, 372–377.
Sonnenschein, C., & Soto, A. (2011). The death of the cancer cell. Cancer Research, 17, 4334–4337.
Soto, A. M., & Sonnenschein, C. (1987). Cell proliferation of estrogen-sensitive cells: The case for negative control. Endocrine Reviews, 8, 44–52.
Soto, A. M., & Sonnenschein, C. (2005a). Emergentism as a default: Cancer as a problem of tissue organization. Journal of Biosciences, 30, 103–118. https://doi.org/10.1007/BF02705155.
Soto, A. M., & Sonnenschein, C. (2005b). Response to Coffman. Bioessays, 27, 460–461. https://doi.org/10.1002/bies.20217.
Soto, A. M., & Sonnenschein, C. (2011). The tissue organizational field theory of cancer: A testable replacement for the somatic mutation theory. Bioessays, 33, 332–340.
Stephens, P. J., et al. (2011). Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell, 144, 27–40. https://doi.org/10.1016/j.cell.2010.11.055.
Stratton, M. R., Campbell, P. J., & Futreal, P. A. (2009). The cancer genome. Nature, 458, 719–724. https://doi.org/10.1038/nature07943.
Tomczak, K., Czerwińska, P., & Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemporary Oncology (Poznan), 19, A68–A77. https://doi.org/10.5114/wo.2014.47136.
Vaux, D. L. (2011). Response to the tissue organization field theory of cancer: A testable replacement for the somatic mutation theory. Bioessays, 33, 660–661. https://doi.org/10.1002/bies.201100063.
Waters, C. K. (2008). Beyond theoretical reduction and layer cake anti-reduction. In M. Ruse (Ed.), The Oxford handbook of philosophy of biology (pp. 238–262). Oxford: Oxford University Press.
Weinberg, R. A. (1998). One renegade cell: How cancer begins. New York: Basic Books.
Weinberg, R. A. (2014). Coming full circle-from endless complexity to simplicity and back again. Cell, 157, 267–271. https://doi.org/10.1016/j.cell.2014.03.004.
Wimsatt, W. C. (2006). Reductionism and its heuristics: Making methodological reductionism honest. Synthese, 151, 445–475. https://doi.org/10.1007/s11229-006-9017-0.
Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA: Harvard University Press.
Wolkenhauer, O., & Green, S. (2013). The search for organizing principles as a cure against reductionism in systems medicine. The FEBS Journal, 280, 5938–5948. https://doi.org/10.1111/febs.12311.
Yusuf, I., & Fruman, D. A. (2003). Regulation of quiescence in lymphocytes. Trends in Immunology, 24, 380–386.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Baxendale, M. Mapping the continuum of research strategies. Synthese 196, 4711–4733 (2019). https://doi.org/10.1007/s11229-018-1683-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-018-1683-1