We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape.
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Research programs are, of course, also influenced by non-epistemic values and social contexts, which possibly cannot be separated from these other aspects (Longino 1990). Especially proponents of a sociology of scientific knowledge looked at conflicts in science as indicators of divergent values, interests and socio-political positions (Barnes 1977). Nonetheless, regarding the articulation by scientists themselves of their own assumptions or those of their opponents, while power relations within science are thematized, social interests from what is perceived as outside of science are rarely mentioned in the debates we look at. Presumably this is the case because these scientists share the epistemic stance of value-free science. Hence even when this is not the case, they often speak as if their differences were not related to social-political positions.
A similar notion of philosophy of nature as part of research programs is discussed in Köchy (2009).
The domain of an inquiry should not be seen as given, but instead as demarcated in the course of inquiry, and such demarcations can change. If we take “field” to refer to a domain and a type of inquiry, characterized by problems posed and techniques employed, two distinct fields can nonetheless share a domain if they share a broader interpretation of the subject matter of their respective different methods of inquiry. This often happens in processes of divergence of fields. Furthermore, when a hierarchical view of fields (and domains) is applied, it is possible to say that two fields, which carve out their domains differently, work on sub-domains of the same domain more broadly conceived and hence can be considered sub-fields of a broader field. For helpful considerations about these notions and their relation, see Shapere (1984). On the ways in which knowledge regarding the choice of research domains, questions and methodologies as well as organizational principles are transferred from other fields in the design of new research projects from which new fields might emerge, see Meunier (2018).
Plurality (of questions, approaches, theories, etc., and, accordingly, disciplines or fields) is a fact about current science, which has to be acknowledged by philosophy of science independently of any view on the question of pluralism as a philosophical position (Kellert et al. 2006). We are concerned here with pluralization, i.e. the processes by which plurality comes about.
What might be called the dynamics of research fields, comprises processes of divergence or pluralization, conflict and cooperation, integration or unification, as well as—not always synchronized—continuities and discontinuities on the level of concepts and practices (see Meunier and Nickelsen 2018). On the diversity of patterns of change, esp. in data-driven life sciences, see also Paul (2009).
According to Georg Simmel one of the hallmarks for competition is that parties compete for a prize awarded by a third party (see Nickelsen 2014, p. 355); the prize, in this case, is acceptance in the double sense of recognition and integration.
On the three categories, epistemic competition, competition for resources and scientific controversy, see also Meunier (2016). Jane Maienschein (2000) speaks of “competing epistemologies” in a related way. Jan Sapp’s (1983) notion of a “struggle for authority” resists a separation of the intellectual and social aspects of competition.
On the relevance of names in discipline formation, see Powell et al. (2007).
Although what is borrowed are often not the specific mathematical approaches of these precursors, but a general “world view”.
This translation is usually not carried out explicitly for lack of the required information to specify a detailed model representing a genome-scale network, apart from the fact that such a model would be computationally intractable. Arguments are often based on toy models whose behaviors are thought to be generic and will likely be exhibited by the network of interest as well.
Statisticians commonly use the term “data reduction” to describe the process of extracting the meaningful parts of a dataset. This is not necessarily related to any form of reductionism (cf. Schaffner 2002). However, in our case study not only the data are reduced, but the obtained statistical summary is also believed to reveal the relevant level of organization. Moreover, information that is neglected in this procedure would arguably not be considered meaningless by other biologists. Feinstein (1999) uses the term “statistical reductionism” in a similar sense in the context of medical data.
Fleck comments on the importance of researchers who are part of more than one community in this respect; see Fleck (1979, Ch. 4).
This is, of course, reminiscent of the point captured by Kuhn’s notion of “incommensurability”. However, Kuhn in this respect had a narrower focus on theories, rather than approaches comprising methods, concepts, models etc. The nature and consequences of incommensurability of theories are much debated; see e.g. Oberheim and Hoyningen-Huene (2018) for an overview, as well as contributions in Soler et al. (2008).
With Fleck (1979, p. 111) one might say that a specialist field is successful if it acquires a circle of general experts (professionals not at the forefront of research but engaged in research-based activities) and possibly even an exoteric circle of educated amateurs. For an account of how scientists make third parties interested in their work and the role of translation in the process, see also Latour (1987).
Because the debates concern fundamental rather than technical issues and address a broader community, they often appear in the opinion sections (commentary, perspectives, letter to the editor, correspondence, etc.) of scientific journals. Again, we wish to emphasize that we do not neglect the role of competition for resources in such debates. Even though it is not discussed in greater length here, we believe that epistemic competition and competition for resources typically go hand in hand.
STS scholars have argued (with reference to the notion of underdetermination of theory by evidence) that controversies—despite the opponents’ belief to the contrary—often cannot be settled empirically, but that closure is instead achieved by social means. Our point here is that while contrahents in controversies at least think the conflict can be settled empirically, in cases of epistemic competition this is not the case. Instead, competitors accuse their opponents of having an inappropriate understanding of the phenomena or being invested in unsuitable methods. Some of the cases discussed in the STS literature might however count as epistemic competition on our account; see e.g. Sismondo (2010, Ch. 11), for a summary of STS work on scientific controversies.
Alon, U. (2007). An Introduction to systems biology: Design principles of biological circuits. Boca Raton: Chapman & Hall.
Andersen, H., & Hepburn, B. (2013). Scientific change. In Internet Encyclopedia of Philosophy. http://www.iep.utm.edu/s-change/. Accessed March 21, 2017.
Ankeny, R. A., & Leonelli, S. (2016). Repertoires: A post-Kuhnian perspective on scientific change and collaborative research. Studies in History and Philosophy of Science, 60, 18–28.
Bapteste, E., Bouchard, F., & Burian, R. M. (2012a). Philosophy and evolution: Minding the gap between evolutionary patterns and tree-like patterns. In M. Anisimova (Ed.), Evolutionary genomics. Statistical and Computational Methods (Vol. 2, pp. 81–110). New York: Springer.
Bapteste, E., & Burian, R. M. (2010). On the need of integrative phylogenomics, and some steps toward its creation. Biology and Philosophy, 25(4), 711–736.
Bapteste, E., & Dupré, J. (2013). Towards a processual microbial ontology. Biology and Philosophy, 28(2), 379–404.
Bapteste, E., Lopez, P., Bouchard, F., Baquero, F., McInerney, J. O., & Burian, R. M. (2012b). Evolutionary analyses of non-genealogical bonds produced by introgressive descent. Proceedings of the National Academy of Sciences, 109(45), 18266–18772.
Bapteste, E., van Iersel, L., Janke, A., Kelchner, S., Kelk, S., McInerney, J. O., et al. (2013). Networks: Expanding evolutionary thinking. Trends in Genetics, 29(8), 439–441.
Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101–113.
Barnes, B. (1977). Interests and the growth of knowledge. London: Routledge.
Bechtel, W., & Richardson, R. C. (2010). Discovering complexity (2nd ed.). Princeton: Princeton University Press.
Beiko, R. G. (2010). Gene sharing and genome evolution: networks in trees and trees in networks. Biology and Philosophy, 25(4), 659–673.
Beiko, R. G. (2011). Telling the whole story in a 10,000-genome world. Biology Direct, 6, 34. https://doi.org/10.1186/1745-6150-6-34.
Boogerd, F. C., Bruggeman, F. J., Hofmeyr, J.-H. S., & Westerhoff, H. V. (2007). Introduction. In F. C. Boogerd, F. J. Bruggeman, J.-H. S. Hofmeyr, & H. V. Westerhoff (Eds.), Systems biology: Philosophical foundations (pp. 3–19). Amsterdam: Elsevier.
Bothwell, J. H. F. (2006). The long past of systems biology. New Phytologist, 170(1), 6–10.
Brenner, S. (2010). Sequences and consequences. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1537), 207–212.
Burian, R. M. (1993). Technique, task definition, and the transition from genetics to molecular genetics: Aspects of the work on protein synthesis in the laboratories of J. Monod and P. Zamecnik. Journal of the History of Biology, 26(3), 387–407.
Burtt, E. A. (1925). The metaphysical foundations of modern physical science. London: Kegan Paul, Trench, Trubner & Co. LTD.
Calvert, J., & Fujimura, J. H. (2011). Calculating life? Duelling discourses in interdisciplinary systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences, 42(2), 155–163.
Comas, I., Moya, A., & González-Candelas, F. (2007). From phylogenetics to phylogenomics: The evolutionary relationships of insect endosymbiotic γ-proteobacteria as a test case. Systematic Biology, 56(1), 1–16.
Corel, E., Lopez, P., Méheust, R., & Bapteste, E. (2016). Network-thinking: Graphs to analyze microbial complexity and evolution. Trends in Microbiology, 24(3), 224–237.
Daston, L., & Galison, P. (2007). Objectivity. New York: Zone Book.
De Backer, P., De Waele, D., & Van Speybroeck, L. (2010). Ins and outs of systems biology vis-a-vis molecular biology: Continuation or clear cut? Acta Biotheoretica, 58(1), 15–49.
Delsuc, F., Brinkmann, H., & Philippe, H. (2005). Phylogenomics and the reconstruction of the tree of life. Nature Reviews Genetics, 6(5), 361–375.
Doolittle, F. W. (2010). The attempt on the life of the tree of life: Science, philosophy and politics. Biology and Philosophy, 25(4), 455–473.
Doolittle, W. F. (2005). If the tree of life fell, would we recognize the sound? In J. Sapp (Ed.), Microbial phylogeny and evolution. Concepts and controversies (pp. 119–133). Oxford: Oxford University Press.
Evans, G. A. (2000). Designer science and the “omic” revolution. Nature Biotechnology, 18(2), 127.
Fagan, M. B. (2011). Waddington redux: Models and explanation in stem cell and systems biology. Biology and Philosophy, 27(2), 179–213.
Fagan, M. B. (2016). Stem cells and systems models: Clashing views of explanation. Synthese, 193(3), 873–907.
Feinstein, A. R. (1999). Statistical reductionism and clinicians’ delinquencies in humanistic research. Clinical Pharmacology and Therapeutics, 66(3), 211–217.
Fleck, L. (1979). Genesis and development of a scientific fact. Chicago: University of Chicago Press.
Forster, D., Bittner, L., Karkar, S., Dunthorn, M., Romac, S., Audic, S., et al. (2015). Testing ecological theories with sequence similarity networks: Marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms. BMC Biology, 13 , 16. https://doi.org/10.1186/s12915-015-0125-5.
Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
Gilbert, W. (1991). Towards a paradigm shift in biology. Nature, 349(6305), 99.
Green, S., Fagan, M., & Jaeger, J. (2015). Explanatory integration challenges in evolutionary systems biology. Biological Theory, 10(1), 18–35.
Gross, F. (2013). The sum of the parts: Heuristic strategies in systems biology. https://air.unimi.it/retrieve/handle/2434/218890/267736/phd_unimi_R08430.pdf. Accessed May 25, 2017.
Hood, L., Rowen, L., Galas, D. J., & Aitchison, J. D. (2008). Systems biology at the Institute for Systems Biology. Briefings in Functional Genomics & Proteomics, 7(4), 239–248.
Huang, S. (2000). The practical problems of post-genomic biology. Nature Biotechnology, 18(5), 471–472.
Huang, S. (2004). Back to the biology in systems biology: What can we learn from biomolecular networks? Briefings in Functional Genomics and Proteomics, 2(4), 279–297.
Huang, S., Eichler, G., Bar-Yam, Y., & Ingber, D. E. (2005). Cell fates as high-dimensional attractor states of a complex gene regulatory network. Physical Review Letters, 94(12), 1–4.
Huang, S., Ernberg, I., & Kauffman, S. (2009). Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Seminars in Cell & Developmental Biology, 20(7), 869–876.
Hull, D. L. (1988). Science as a process: An evolutionary account of the social and conceptual development of science. Chicago: University of Chicago Press.
Huson, D. H., Rupp, R., & Scornavacca, C. (2010). Phylogenetic networks. Concepts, algorithms and application. Cambridge: Cambridge University Press.
Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. New York: Oxford University Press.
Kellert, S. H., Longino, H. E., & Waters, C. K. (2006). Introduction: The pluralist stance. In S. H. Kellert, H. E. Longino, & C. K. Waters (Eds.), Scientific pluralism (pp. vii–xxix). Minneapolis: University of Minnesota Press.
Kitano, H. (2002). Computational systems biology. Nature, 420(6912), 206–210.
Köchy, K. (2009). Naturphilosophie ist mehr als angewandte Wissenschaftstheorie. In C. Kummer (Ed.), Was ist Naturphilosophie und was kann sie leisten? (pp. 38–56). Alber: Freiburg & München.
Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed., enlarged). Chicago: University of Chicago Press.
Lakatos, I. (1978). The methodology of scientific research programmes (Vol. 1). Cambridge: Cambridge University Press.
Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Cambridge: Harvard University Press.
Laudan, L. (1977). Progress and its problems: Towards a theory of scientific growth. Berkeley: University of California Press.
Leonelli, S. (2016). Data-centric biology: A philosophical study. Chicago: University of Chicago Press.
Longino, H. E. (1990). Science as social knowledge: Values and objectivity in scientific inquiry. Princeton: Princeton University Press.
Lopez, P., Halary, S., & Bapteste, E. (2015). Highly divergent ancient gene families in metagenomics samples are compatible with additional divisions of life. Biology Direct, 10, 64. https://doi.org/10.1186/s13062-015-0092-3.
Loscalzo, J., & Barabási, A.-L. (2011). Systems biology and the future of medicine. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(6), 619–627.
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.
MacLeod, M., & Nersessian, N. J. (2016). Interdisciplinary problem-solving: Emerging modes in integrative systems biology. European Journal for Philosophy of Science, 6(3), 401–418.
Maienschein, J. (2000). Competing epistemologies and developmental biology. In R. Creath & J. Maienschein (Eds.), Biology and epistemology (pp. 122–137). Cambridge: Cambridge University Press.
Marcum, J. A. (2008). Does systems biology represent a Kuhnian paradigm shift? New Phytologist, 179(3), 587–589.
Merz, M., & Sormani, P. (Eds.). (2015). The local configuration of new research fields: On regional and national diversity. Dordrecht: Springer.
Meunier, R. (2016). Epistemic competition between developmental biology and genetics around 1900: Traditions, concepts and causation. NTM Zeitschrift für Geschichte der Wissenschaften, Technik und Medizin, 24(2), 141–167.
Meunier, R. (2018). Project knowledge and its resituation in the design of research projects: Seymour Benzer’s behavioral genetics, 1965–1974. Studies in History and Philosophy of Science. https://doi.org/10.1016/j.shpsa.2018.04.001
Meunier, R., & Nickelsen, K. (2018). New perspectives in the history of twentieth-century life sciences: Historical, historiographical and epistemological themes. History and Philosophy of the Life Sciences, 40, 19.
Mindell, D. P. (2013). The tree of life: Metaphor, model, and heuristic device. Systematic Biology, 62(3), 479–489.
Morrison, D. A. (2011). Introduction to phylogenetic networks. Uppsala: RJR Productions.
Morrison, D. A. (2014). Is the tree of life the best metaphor, model, or heuristic for phylogenetics? Systematic Biology, 63(4), 628–638.
Nersessian, N. J., & Chandrasekharan, S. (2009). Hybrid analogies in conceptual innovation in science. Cognitive Systems Research, 10(3), 178–188.
Nickelsen, K. (2014). Kooperation und Konkurrenz in den Naturwissenschaften. In R. Jessen (Ed.), Konkurrenz in der Geschichte: Praktiken - Werte - Institutionalisierungen (pp. 333–379). Frankfurt am Main: Campus Verlag.
O’Malley, M. A. (2010). Ernst Mayr, the tree of life, and philosophy of biology. Biology and Philosophy, 25(4), 529–552.
O’Malley, M. A. (2013). When integration fails: Prokaryote phylogeny and the tree of life. Studies in History and Philosophy of Biological and Biomedical Sciences, 44(4), 551–562.
O’Malley, M. A. (2014). Philosophy of microbiology. Cambridge: Cambridge University Press.
O’Malley, M. A., & Boucher, Y. (2005). Paradigm change in evolutionary microbiology. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(1), 183–208.
O’Malley, M. A., & Dupré, J. (2005). Fundamental issues in systems biology. BioEssays, 27(12), 1270–1276.
O’Malley, M. A., & Koonin, E. V. (2011). How stands the tree of life a century and a half after the origin? Biology Direct, 6, 32. https://doi.org/10.1186/1745-6150-6-32.
O’Malley, M. A., Martin, W., & Dupré, J. (2010). The tree of life: Introduction to an evolutionary debate. Biology and Philosophy, 25(4), 441–453.
Oberheim, E., & Hoyningen-Huene, P. (2018). The incommensurability of scientific theories. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2018). https://plato.stanford.edu/entries/incommensurability/. Accessed November 16, 2018.
Palsson, B. Ø. (2006). Systems biology: Properties of reconstructed networks. Cambridge: Cambridge University Press.
Paul, N. W. (2009). Rationalitäten der Wissenproduktion: Über Transformationen von Gegenständen, Technologien und Information in Biomedizin und Lebenswissenschaften. Berichte Zur Wissenschaftsgeschichte, 32(3), 230–245.
Pellens, R., & Grandcolas, P. (2016). Biodiversity Conservation and Phylogenetic Systematics. Preserving our evolutionary heritage in an extinction crisis. Berlin: Springer.
Poon, W. C. K. (2011). Interdisciplinary reflections: The case of physics and biology. Studies in History and Philosophy of Biological and Biomedical Sciences, 42(2), 115–118.
Powell, A., & Dupré, J. (2009). From molecules to systems: The importance of looking both ways. Studies in History and Philosophy of Biological and Biomedical Sciences, 40(1), 54–64.
Powell, A., O’Malley, M. A., Müller-Wille, S., Calvert, J., & Dupré, J. (2007). Disciplinary baptisms: A comparison of the naming stories of genetics, molecular biology, genomics, and systems biology. History and Philosophy of the Life Sciences, 29(1), 5–32.
Purvis, A., Gittleman, J. L., & Brooks, T. (2005). Phylogeny and conservation. Cambridge: Cambridge University Press.
Rheinberger, H.-J. (1997). Toward a history of epistemic things: Synthesizing proteins in the test tube. Stanford: Stanford University Press.
Rheinberger, H.-J. (2009). Recent science and its exploration: The case of molecular biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 40(1), 6–12.
Sapp, J. (1983). The struggle for authority in the field of heredity, 1900–1932: New perspectives on the rise of genetics. Journal of the History of Biology, 16(3), 311–342.
Sauer, T., & Scholl, R. (2016). The philosophy of historical case studies. New York: Springer.
Schaffner, K. F. (2002). Reductionism, complexity and molecular medicine: Genetic chips and the ‘globalization’ of the genome. In M. H. V. Van Regenmortel & D. A. Hull (Eds.), Promises and limits of reductionism in the biomedical sciences (pp. 323–347). Hoboken: Wiley.
Shapere, D. (1984). Reason and the search for knowledge: Investigations in the philosophy of science. Dordrecht: Reidel.
Sismondo, S. (2010). An introduction to science and technology studies (2nd ed.). Malden, MA: Wiley.
Soler, L., Sankey, H., & Hoyningen-Huene, P. (Eds.). (2008). Rethinking scientific change and theory comparison: Stabilities, ruptures, incommensurabilities? Dordrecht: Springer.
Stevens, C. F. (2004). Systems biology versus molecular biology. Current Biology, 14(2), 51–52.
Strohman, R. C. (1997). The coming Kuhnian revolution in biology. Nature Biotechnology, 15(3), 194–200.
Velasco, J. D. (2012). The future of systematics: Tree thinking without the tree. Philosophy of Science, 79(5), 624–636.
Völkel, F., Bapteste, E., Habib, M., Lopez, P., & Vigliotti, C. (2016). Read networks and k-laminar graphs. Computing Research Repository. arXiv:1603.01179
Volkmann, L., Martyn, I., Moulton, V., Spillner, A., & Mooers, A. O. (2014). Prioritizing populations for conservation using phylogenetic networks. PLoS ONE, 9(2), e88945. https://doi.org/10.1371/journal.pone.0088945.
Waddington, C. H. (1957). The strategy of the genes: A discussion of some aspects of theoretical biology. London: Allen & Unwin.
Weingart, P. (1974). On a sociological theory of scientific change. In R. Whitley (Ed.), Social processes of scientific development (pp. 45–68). London: Law Book Co of Australasia.
Yook, S.-H., Oltvai, Z. N., & Barabási, A.-L. (2004). Functional and topological characterization of protein interaction networks. Proteomics, 4(4), 928–942.
This paper was written in the context of the International Biophilosophical School (University of Padua, 27-30 April 2015) as part of the “Integrative Biophilosophy” research project located at the University of Kassel. Funding by the DAAD (German Academic Exchange Service) is gratefully acknowledged. We would like to thank Kristian Köchy, Pierre-Luc Germain, the Editor of the Journal and an anonymous reviewer for helpful comments. R.M. wishes to acknowledge the hospitality of the Institute for Cultural Inquiry (ICI) Berlin where he was an affiliated fellow while revising the paper.
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Gross, F., Kranke, N. & Meunier, R. Pluralization through epistemic competition: scientific change in times of data-intensive biology. HPLS 41, 1 (2019). https://doi.org/10.1007/s40656-018-0239-5