Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Introduction
Data Acquisition
Discussion
The Radical Materiality of Biological Phenomena
Conclusion
References
FAQs
All Topics
Philosophy
Philosophy of Science

Measurement in biology is methodized by theory

Profile image of Maël MontévilMaël Montévil

2019, Biology & Philosophy

https://doi.org/10.1007/S10539-019-9687-X
visibility

…

description

15 pages

link

2 files

Abstract

We characterize access to empirical objects in biology from a theoretical perspective. Unlike objects in current physical theories, biological objects are the result of a history and their variations continue to generate a history. This property is the starting point of our concept of measurement. We argue that biological measurement is relative to a natural history which is shared by the different objects subjected to the measurement and is more or less constrained by biologists. We call symmetrization the theoretical and often concrete operation which leads to considering biological objects as equivalent in a measurement. Last, we use our notion of measurement to analyze research strategies. Some strategies aim to bring biology closer to the epistemology of physical theories, by studying objects as similar as possible, while others build on biological diversity.

Key takeaways
sparkles

AI

  1. Biological measurement is relative to shared natural histories, differing from physical measurement's theoretical invariants.
  2. Symmetrization is crucial for treating biological objects as equivalent in measurement, accommodating their historical context.
  3. Research strategies range from genericization of specimens to preserving biological diversity, impacting reproducibility.
  4. Historical context and genealogical control are vital for understanding biological variability during experiments.
  5. Biological objects display radical materiality, requiring empirical knowledge tied to specific historical instances.

Related papers

Rehabilitating Biology as a Natural History
Takashi Ikegami

Adaptive Behavior, 2009

View PDFchevron_right
Experimental philosophy of biology: notes from the field
Karola Stotz

Studies in History and Philosophy of Science, 2009

I use a recent 'experimental philosophy' study of the concept of the gene conducted by myself and collaborators to discuss the broader epistemological framework within which that research was conducted, and to reflect on the relationship between science, history and philosophy of science, and society.

View PDFchevron_right
The Philosophy of Biology
Christopher J. Austin

Analysis, 2017

View PDFchevron_right
The Notion of Scientific Knowledge in Biology
Silvia Morante

Science & Education, 2016

View PDFchevron_right
Recent Work in The Philosophy of Biology
Christopher J. Austin

2017

The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that tradition hasn't received the wider attention it so thoroughly deserves. This review will briefly introduce some recent significant topics of debate within the philosophy of biology, focusing on those whose metaphysical themes (in everything from composition to causation) are likely to be of wide-reaching, cross-disciplinary interest.

View PDFchevron_right
1Understanding the foundations of biology: A research program
Jean-Louis Sikorav

2016

Biology is a scientific discipline based on phenomena derived from observations or experi-ments, on concepts elaborated to understand them, and on further generalizations of these concepts through theories or laws of nature. We present here a research program seeking to build the foundations of biology in terms of elements, logic and principles, using both the language and the general methods employed in other disciplines. For this, we define four philosophical attitudes towards the concepts of symmetry and asymmetry and of necessity and contingency and explain how they enter into the foundations of knowledge and in the construction of theories. The customary introduction of compound biological entities such as organisms, cells, genes, catalysts or motors, is largely descriptive and relies mostly on inductive reasoning. We endeavor to complement this conventional approach with a more constructive one based on an explicit and more complete use of logical infer-ences. This program is ...

View PDFchevron_right
Theoretical Principles of Relational biology Space, time, organization
Angelo Marinucci

2023

This book proposes the foundation of the relational approach to biology, rejecting the deterministic and reductionist approach of molecular biology. Although biology has made enormous progress in the last seventy years, onto genesis is still conceived as a “revelation” of information (DNA). Recovering the geometric tradition, relational biology conceives scientific and epistemological tools (cause, probability, space etc.) of science in a new way. If probabilistic biology and organicism still proposes a biology based on physics, with a fundamental invariant, relational biology is based on variation: its fundamental invariant is variation, one of the most important elements of life. This is an indispensable book for academics who consider biology from a new theoretical approach, in particular for those working in the domains of cancer, ontogenesis and evolution.

View PDFchevron_right
Ideal Observations: Information and Causation in Biological Practice
Oliver M Lean
View PDFchevron_right
Michael Ruse, ed. , The Oxford Handbook of Philosophy of Biology . Reviewed by
Glenn Branch

Philosophy in Review, 2011

View PDFchevron_right
What methods do life scientists use? A brief history with philosophical implications
Erik L Peterson

Philosophy of Biology for Biologists, 2020

This chapter is about the methods used in the bio-sciences. In it, I present a case that there are a limited number of methods that biologists employ. On its face, this conclusion seems neither exciting nor novel; that is, unless we consider that the life sciences have long been engaged in a hidden battle between two opposing views regarding the number of scientific methods that exist. I am arguing against both sides. The two standard positions that I am arguing against are: (1) that there exists a single, if idealized, Scientific Method (i.e., unity), and (2) that the sciences are individualistic and, therefore, scientists operate without a common method (i.e., anarchy). I am asserting that in contrast to unity or anarchy, there are several but a limited number of methods possible in the bio-sciences. By combining general methods and submethods, there could be over a dozen possible valid means by which to conduct a life sciences inquiry.

View PDFchevron_right

References (66)

  1. Abolins, S. R., Pocok, M., Hafalla, J., Riley, E., & Viney, M. (2010). Measures of immune function of wild mice, Mus musculus. Molecular Ecology, 20, 881-892. doi:10.1111/j.1365-294X.2010.04910.x.
  2. Bailly, F., & Longo, G. (2011). Mathematics and the natural sciences; The Physical Singularity of Life. London: Imperial College Press.
  3. Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533, 452-454. doi:10.1038/533452a.
  4. Baxendale, M. (2018). Mapping the continuum of research strategies. Synthese, . doi:10.1007/s11229-018-1683-1.
  5. Beatty, J. (1995). The evolutionary contingency thesis. In Conceptual Issues in Evolutionary Biology. (p. 217). Cambridge, MA: MIT Press.
  6. Begley, C., & Ioannidis, J. (2014). Reproducibility in science. Circula- tion Research, 116 , 116-126. doi:10.1161/CIRCRESAHA.114.303819.
  7. Begley, C. G., & Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature, 483, 531-533. doi:10.1038/483531a.
  8. Bolland, M. J., Avenell, A., Gamble, G. D., & Grey, A. (2016). System- atic review and statistical analysis of the integrity of 33 randomized controlled trials. Neurology, . doi:10.1212/WNL.0000000000003387.
  9. Braun, E. (2015). The unforeseen challenge: from genotype-to- phenotype in cell populations. Reports on Progress in Physics, 78, 036602. doi:10.1088/0034-4885/78/3/036602.
  10. Cai, L., Friedman, N., & Xie, X. (2006). Stochastic protein expression in individual cells at the single molecule level. Nature, 440, 358 - 362. doi:10.1038/nature04599.
  11. Chang, H. (2004). Inventing temperature: Measurement and scientific progress. Oxford University Press.
  12. Chia, R., Achilli, F., Festing, M., & Fisher, E. (2005). The origins and uses of mouse outbred stocks. Nature genetics, 37 , 1181. doi:10. 1038/ng1665.
  13. CZN International (1999). International code of zoological nomenclature. London: International Trust for Zoological Nomenclature.
  14. Danchin, E., Pocheville, A., Rey, O., Pujol, B., & Blanchet., S. (In press). Epigenetics as the hub guiding nongenetic inheritance to genetic assimilation. Biological Reviews, .
  15. Dudley, J., Golovkina, T., & Ross, S. (2016). Lessons learned from mouse mammary tumor virus in animal models. ILAR Journal, 57 , 12-23. doi:10.1093/ilar/ilv044.
  16. Festing, M. (2014). Evidence should trump intuition by preferring in- bred strains to outbred stocks in preclinical research. ILAR Journal, 55, 399-404. doi:10.1093/ilar/ilu036.
  17. Feynman, R., & Gleick, J. (1967). The character of physical law. MIT press.
  18. Gilbert, S. F. (2014). A holobiont birth narrative: The epigenetic transmission of the human microbiome. Frontiers in Genetics, 5. doi:10.3389/fgene.2014.00282.
  19. Gilbert, S. F., & Epel, D. (2009). Ecological developmental biology: inte- grating epigenetics, medicine, and evolution. Sinauer Associates Sun- derland.
  20. Gillies, D. (2012). Philosophical theories of probability. Routledge.
  21. Gould, S. (2002). The structure of evolutionary theory. Harvard University Press.
  22. Grandcolas, P. (2017). Loosing the connection between the ob- servation and the specimen: a by-product of the digital era or a trend inherited from general biology? Bionomina, 12, 57-62. doi:10.11646/bionomina.12.1.7.
  23. Gross, V., & Luft, F. (2003). Exercising restraint in measuring blood pressure in conscious mice. Hypertension, 41, 879-881. doi:10.1161/ 01.HYP.0000060866.69947.D1.
  24. Heindel, J., Newbold, R., Bucher, J., Camacho, L., Delclos, K. B., Lewis, S. M., Vanlandingham, M., Churchwell, M. I., Twaddle, N. C., McLellen, M., Chidambaram, M., Bryant, M., Woodling, K., da Costa, G. G., Ferguson, S. A., Flaws, J., Howard, P. C., Walker, N. J., Zoeller, R. T., Fostel, J., Favaro, C., & Schug, T. T. (2015). NIEHS/FDA CLARITY-BPA research program update. Reproductive Toxicology, 58, 33 -44. doi:10.1016/j.reprotox.2015. 07.075.
  25. Houle, D., Pélabon, C., Wagner, G. P., & Hansen, T. F. (2011). Mea- surement and meaning in biology. The Quarterly Review of Biology, 86 , 3-34. doi:10.1086/658408.
  26. Huneman, P. (2018). Diversifying the picture of explanations in bi- ological sciences: ways of combining topology with mechanisms. Synthese, 195, 115-146. doi:10.1007/s11229-015-0808-z.
  27. Isaacs, J. T. (1986). Genetic control of resistance to chemically in- duced mammary adenocarcinogenesis in the rat. Cancer Research, 46 , 3958-3963.
  28. Jablonka, E., Lamb, M. J., & Zeligowski, A. (2014). Evolution in Four Dimensions, revised edition: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. London: MIT press.
  29. Jablonka, E., & Raz, G. (2009). Transgenerational epigenetic inheri- tance: prevalence, mechanisms, and implications for the study of heredity and evolution. The Quarterly review of biology, 84, 131-176. doi:10.1086/598822.
  30. Jensen, V. S., Porsgaard, T., Lykkesfeldt, J., & Hvid, H. (2016). Rodent model choice has major impact on variability of standard preclinical readouts associated with diabetes and obesity research. American journal of translational research, 8, 3574.
  31. Johnson, P. D., & Besselsen, D. G. (2002). Practical aspects of ex- perimental design in animal research. ILAR journal, 43, 202-206. doi:10.1093/ilar.43.4.202.
  32. Kohler, R. E. (1994). Lords of the fly: Drosophila genetics and the experi- mental life. University of Chicago Press.
  33. Lancet (2018). Uk life science research: time to burst the biomedical bubble. The Lancet, 392, 187. doi:10.1016/S0140-6736(18)31609-X.
  34. Lecointre, G., & Le Guyader, H. (2006). The tree of life: a phylogenetic classification volume 20. Harvard University Press.
  35. Leonelli, S. (2014). What difference does quantity make? on the epistemology of big data in biology. Big Data & Society, 1, 2053951714534395. doi:10.1177/2053951714534395.
  36. Lindner, A., Madden, R., Demarez, A., Stewart, E., & Taddei, F. (2008). Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation. Proceedings of the National Academy of Sciences, 105, 3076 -3081. doi:10.1073/pnas.0708931105.
  37. Longo, G., & Montévil, M. (2011a). From physics to biology by extending criticality and symmetry breakings. Progress in Biophysics and Molecular Biology, 106 , 340 -347. doi:10.1016/j.pbiomolbio. 2011.03.005.
  38. Longo, G., & Montévil, M. (2011b). Protention and retention in biological systems. Theory in Biosciences, 130, 107-117. doi:10.1007/ s12064-010-0116-6.
  39. Longo, G., & Montévil, M. (2014). Perspectives on Organisms: Biological time, symmetries and singularities. Lecture Notes in Morphogenesis. Dordrecht: Springer. doi:10.1007/978-3-642-35938-5.
  40. Longo, G., & Montévil, M. (2017). Comparing symmetries in models and simulations. In M. Dorato, L. Magnani, & T. Bertolotti (Eds.), Springer Handbook of Model-Based Science. Springer. doi:10.1007/ 978-3-319-30526-4.
  41. McNeill, J., Barrie, F., Buck, W., Demoulin, V., Greuter, W., Hawksworth, D., Herendeen, P., Knapp, S., Marhold, K., Prado, J. et al. (2012). International Code of Nomenclature for algae, fungi and plants. Konigstein, Germany: Koeltz Scientific Books.
  42. Mogil, J. S., Wilson, S. G., Bon, K., Lee, S. E., Chung, K., Raber, P., Pieper, J. O., Hain, H. S., Belknap, J. K., Hubert, L., Elmer, G. I., Chung, J. M., & Devor, M. (1999). Heritability of nociception i: Responses of 11 inbred mouse strains on 12 measures of nociception. Pain, 80, 67 -82. doi:10.1016/S0304-3959(98)00197-3.
  43. Montévil, M. (2018). Possibility spaces and the notion of novelty: from music to biology. Synthese, . doi:10.1007/s11229-017-1668-5.
  44. Montévil, M., & Mossio, M. (2015). Biological organisation as closure of constraints. Journal of Theoretical Biology, 372, 179 -191. doi:10. 1016/j.jtbi.2015.02.029.
  45. Montévil, M., Mossio, M., Pocheville, A., & Longo, G. (2016). Theoret- ical principles for biology: Variation. Progress in Biophysics and Molec- ular Biology, 122, 36 -50. doi:10.1016/j.pbiomolbio.2016.08.005.
  46. Morgan, M. (2002). Experiments without material intervention: Model experiments, virtual experiments and virtually experiments. In H. Radder (Ed.), Scientific Experimentation and Its Philosophical Significance. Pittsburgh University Press.
  47. Mossio, M., Montévil, M., & Longo, G. (2016). Theoretical princi- ples for biology: Organization. Progress in Biophysics and Molecular Biology, 122, 24 -35. doi:10.1016/j.pbiomolbio.2016.07.005.
  48. Mugur-Schächter, M. (2002). Objectivity and descriptional relativities. Foundations of Science, 7 , 73 -180. doi:10.1023/A:1016095424229.
  49. Nadin, M. (2017). Rethinking the experiment: necessary (r) evolution. AI & SOCIETY , (pp. 1-19). doi:10.1007/s00146-017-0705-8.
  50. Patterson, F., & Linden, E. (1981). The education of Koko. Holt, Rinehart, and Winston.
  51. de Queiroz, K. (1992). Phylogenetic definitions and taxonomic philos- ophy. Biology and Philosophy, 7 , 295-313. doi:10.1007/BF00129972.
  52. Ryan, B. C., & Vandenbergh, J. G. (2002). Intrauterine position effects. Neuroscience & Biobehavioral Reviews, 26 , 665 -678. doi:10.1016/ S0149-7634(02)00038-6.
  53. Sacca, R., Elder, B., & Wasson, K. (2013). The C57BL/6 Mouse: The Role of the C57BL/6N Mouse in the Creation of Future Genetically Engineered Models. Charles River Laboratories International, Inc.
  54. Salam, A. (1990). Unification of Fundamental Forces. Cambridge Uni- versity Press.
  55. Schrödinger, E. (1944). What Is Life?. Cambridge U.P. Simpson, E. M., Linder, C. C., Sargent, E. E., Davisson, M. T., Mo- braaten, L. E., & Sharp, J. J. (1997). Genetic variation among 129 substrains and its importance for targeted mutagenesis in mice. Na- ture genetics, 16 , 19. doi:10.1038/ng0597-19.
  56. Solter, D. (1998). Dolly is a clone-and no longer alone. Nature, 394, 315. Soto, A. M., Justicia, H., Wray, J. W., & Sonnenschein, C. (1991). p- nonyl-phenol: An estrogenic xenobiotic released from "modified" polystyrene. Environmental Health Perspectives, 92, 167-173. doi:10. 1289/ehp.9192167.
  57. Soto, A. M., Longo, G., Montévil, M., & Sonnenschein, C. (2016a). The biological default state of cell proliferation with variation and motility, a fundamental principle for a theory of organisms. Progress in Biophysics and Molecular Biology, 122, 16 -23. doi:10.1016/j. pbiomolbio.2016.06.006.
  58. Soto, A. M., Longo, G., Noble, D., Perret, N., Montévil, M., Sonnen- schein, C., Mossio, M., Pocheville, A., Miquel, P.-A., Hwang, S.-Y. et al. (2016b). From the century of the genome to the century of the organism: New theoretical approaches. Progress in Biophysics and Molecular Biology, Special issue, (pp. 1-82).
  59. Spearow, J. L., Doemeny, P., Sera, R., Leffler, R., & Barkley, M. (1999). Genetic variation in susceptibility to endocrine disruption by estrogen in mice. Science, 285, 1259-1261.
  60. Stewart, E., Madden, R., Paul, G., & Taddei, F. (2005). Aging and death in an organism that reproduces by morphologically symmetric division. PLoS Biology, 3, e45. doi:10.1371/journal.pbio.0030045.
  61. Susiarjo, M., Hassold, T. J., Freeman, E., & Hunt, P. A. (2007). Bisphe- nol a exposure in utero disrupts early oogenesis in the mouse. PLoS genetics, 3, e5. doi:10.1371/journal.pgen.0030005.
  62. Van Fraassen, B. (1989). Laws and symmetry. Oxford University Press, USA. Wagner, G. P. (2010). The measurement theory of fitness. Evolution, 64, 1358-1376. doi:10.1111/j.1558-5646.2009.00909.x.
  63. Weber, M. (2004). Philosophy of experimental biology. Cambridge uni- versity press.
  64. West, B. (2006). Where medicine went wrong: Rediscovering the path to complexity. Teaneck, NJ: World Scientific.
  65. West-Eberhard, M. J. (2003). Developmental plasticity and evolution. Oxford University Press.
  66. Wilmut, I., Schnieke, A. E., McWhir, J., Kind, A. J., & Campbell, K. H. (1997). Viable offspring derived from fetal and adult mammalian cells. Nature, 385, 810-813. doi:10.1038/385810a0.

FAQs

sparkles

AI

What are the limitations of biological measurements compared to physical measurements?add

The paper reveals that biological measurements are influenced by historical context, making them more variable than physical measurements, which rely on stable mathematical structures.

How does historicity affect reproducibility in biological experiments?add

The research finds that the principle of variation in biology suggests that reproducibility is complicated by the continuous evolution of biological objects and their respective contexts.

What implications does symmetrization have for biological measurement strategies?add

The study demonstrates that symmetrization, both concrete and epistemic, determines how biological objects are treated in measurements, impacting their reproducibility and relevance in experimental settings.

How do genealogical contexts influence biological measurements?add

The findings suggest that controlled genealogies provide insights into the shared past of specimens, thus ensuring that biological measurements are contextually relevant and meaningful.

What distinguishes case study measurements from traditional biological measurements?add

The paper illustrates that case studies focus on singular instances without aiming for reproducibility, highlighting unique biological phenomena, unlike traditional methods that strive for generalizable results.

Related papers

Measurement and meaning in biology
Gunter Wagner

2011

ABSTRACT Measurement-the assignment of numbers to attributes of the natural world-is central to all scientific inference. Measurement theory concerns the relationship between measurements and reality; its goal is ensuring that inferences about measurements reflect the underlying reality we intend to represent. The key principle of measurement theory is that theoretical context, the rationale for collecting measurements, is essential to defining appropriate measurements and interpreting their values.

View PDFchevron_right
Historicity at the heart of biology
Maël Montévil

Theory in Biosciences , 2020

Most mathematical modeling in biology relies either implicitly or explicitly on the epistemology of physics. The underlying conception is that the historicity of biological objects would not matter to understand a situation here and now, or, at least, historicity would not impact the method of modeling. We analyze that it is not the case with concrete examples. Historicity forces a conceptual reconfiguration where equations no longer play a central role. We argue that all observations depend on objects defined by their historical origin instead of their relations as in physics. Therefore, we propose that biological variations and historicity come first, and regularities are constraints with limited validity in biology. Their proper theoretical and empirical use requires specific rationales.

View PDFchevron_right
The Limits of Measuring Information in Biology: An Ontological Approach
Agustin Mercado, Alfonso Arroyo-Santos

The concept of biological information, and information in general, usually presupposes a purely quantitative view of reality. Even though actualist quantification has an important place in the description of the world, a nominalistic stance that tries to simplify reality in purely actualist terms inevitably runs into inconsistencies; these inconsistencies have been pointed out by the critical assessments of the notion of biological information. Rather than calling for an abandonment of the informational terminology, we try to rethink information as a part of an event, the description of which cannot be exhausted by a physicalist, mechanistic, temporally static view of reality. Reconceptualizing the notion of biological information in terms of a process of interpretation rather than as an informational object allows us to transcend the limitations imposed by an analysis of reality that depends on a fixed, finite structure of outcome. Thus, we argue that measuring biological information is intrinsically problematic.

View PDFchevron_right
The Identity of Organisms in Scientific Practice: Integrating Historical and Relational Conceptions
Maël Montévil, Matteo Mossio

Frontiers in physiology, 2020

We address the identity of biological organisms at play in experimental and modeling practices. We first examine the central tenets of two general conceptions, and we assess their respective strengths and weaknesses. The historical conception, on the one hand, characterizes organisms' identity by looking at their past, and specifically at their genealogical connection with a common ancestor. The relational conception, on the other hand, interprets organisms' identity by referring to a set of distinctive relations between their parts, and between the organism and its environment. While the historical and relational conceptions are understood as opposed and conflicting, we submit that they are also fundamentally complementary. Accordingly, we put forward a hybrid conception, in which historical and relational (and more specifically, organizational) aspects of organisms' identity sustain and justify each other. Moreover, we argue that organisms' identity is not only hybrid but also bounded, insofar as the compliance with specific identity criteria tends to vanish as time passes, especially across generations. We spell out the core conceptual framework of this conception, and we outline an original formal representation. We contend that the hybrid and bounded conception of organisms' identity suits the epistemological needs of biological practices, particularly with regards to the generalization and reproducibility of experimental results, and the integration of mathematical models with experiments.

View PDFchevron_right
Laws, Models, and Theories in Biology: A Unifying Interpretation
Pablo Lorenzano

Springer eBooks, 2020

Author's personal copy 164 scientific practice (Achinstein 1968; Hesse 1966; Harré 1970), as well as investigating what role analogies and metaphors play in the construction of models (Black 1962; Hesse 1966) or of other components, linked to these, raised by historicist philosophers, such as exemplars (Kuhn 1970). At the present time, the importance of models in scientific practice is being emphasized. The semantic view-which deals with the subject matter of models within the framework of a general conception of scientific theories-is being imposed as an alternative to the classical and historicist views of scientific theories, 1 and model views of science are being developed-which deal with questions of the relationship between models and experience and between models and general theories independently of a general metatheory of science (

View PDFchevron_right

Related topics

  • Evolutionary Biology
  • Theoretical Physics
  • Philosophy of Science
  • Philosophy of Biology
  • Biology
  • Reproducibility of Results

    • Cited by

    Science Education in the Light of COVID-19
    Michael J Reiss

    Science & Education

    In this position paper, I examine how the history, philosophy and sociology of science (HPS) can contribute to science education in the era of the COVID-19 pandemic. I discuss shortcomings in the ways that history is often used in school science, and examine how knowledge of previous pandemics might help in teaching about COVID-19. I look at the potential of issues to do with measurement in the context of COVID-19 (e.g. measurement of mortality figures) to introduce school students to issues about philosophy of science, and I show how COVID-19 has the affordance to broaden and deepen the moral philosophy that students typically meet in biology lessons. COVID-19 also provides opportunities to introduce students to sociological ways of thinking, examining data and questioning human practices. It can also enable students to see how science, economics and politics inter-relate. In the final part of the paper, I suggest that there are strong arguments in favour of an interdisciplinary approach in tackling zoonoses like COVID-19 and that there is much to be said for such interdisciplinarity in school science lessons when teaching about socioscientific issues and issues intended to raise scientific literacy.

    View PDFchevron_right
    Biopolitica ed ecologia
    Andrea Angelini

    Premio Ricerca «Città di Firenze»

    One of the core issues in Michel Foucault’s and Georges Canguilhem’s works is the study of the epistemological status and political functioning of biological discourse, explored in its fully heterogeneous, plural, and conflictual character. Starting from the relations and tensions between their theoretical orientations, the present work attempts a critical re-reading of their researchs, with the aim of integrating and mobilizing their analysis in light of contemporary political and epistemological debates. In this respect, Canguilhem’s biological philosophy allows us to re-examine the foucauldian conceptions of history, society, subjectivity, technology, and environment; moreover, it enables a re-questioning about the spaces of intervention of biopolitical technologies from a socio-ecological and eco-historical perspective.

    View PDFchevron_right
    Progress in integrative systems biology, physiology and medicine: towards a scale-relative biology
    Philip turner

    The European Physical Journal A, 2020

    In this paper we present a review of progress in addressing the challenge to understand and describe the vast complexity and multi-level organisation associated with biological systems. We begin with a review of past and current approaches, key lessons, and unresolved challenges, which require a new conceptual framework to address them. After summarizing the core of the problem, which is linked to computational complexity, we review recent developments within the theoretical framework of scale relativity, which offers new insights into the emergence of structure and function (at multiple scales), providing a new integrative approach to biological systems. The theoretical framework describes the critical role of thermodynamics and quantum vacuum fluctuations in the emergence of charge-induced macroscopic quantum fields (effectively a new quantum field theory) at multiple scales, which underpin a macroscopic quantum description of biological systems as a complex exemplar of condensed ...

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved