Skip to main content

Advertisement

SpringerLink
Log in

Waddington redux: models and explanation in stem cell and systems biology

  • Published:
Biology & Philosophy Aims and scope Submit manuscript

Abstract

Stem cell biology and systems biology are two prominent new approaches to studying cell development. In stem cell biology, the predominant method is experimental manipulation of concrete cells and tissues. Systems biology, in contrast, emphasizes mathematical modeling of cellular systems. For scientists and philosophers interested in development, an important question arises: how should the two approaches relate? This essay proposes an answer, using the model of Waddington’s landscape to triangulate between stem cell and systems approaches. This simple abstract model represents development as an undulating surface of hills and valleys. Originally constructed by C. H. Waddington to visually explicate an integrated theory of genetics, development and evolution, the landscape model can play an updated unificatory role. I examine this model’s structure, representational assumptions, and uses in all three contexts, and argue that explanations of cell development require both mathematical models and concrete experiments. On this view, the two approaches are interdependent, with mathematical models playing a crucial but circumscribed role in explanations of cell development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Thanks to an anonymous reviewer for Biology and Philosophy for pushing me to clarify this point.

  2. In this respect, systems biology reprises some of the reductionistic tendencies of mid-twentieth century molecular biology—and for similar reasons. Microorganisms are simpler and more tractable, and more of the necessary data for constructing models is available. However, systems models per se are not limited to microorganisms, and indeed a number of multicellular phenomena have been modeled as well: including early development of fly and sea urchin, mammalian blood cell populations, heart muscle, and regenerating intestinal epithelium. Thanks to two anonymous reviewers for Biology and Philosophy for highlighting this issue.

  3. E.g., Machamer et al. (2000), Glennan (2002), Craver (2007) and Bechtel (2008). The latter two authors, in particular, emphasize the hierarchical and constitutive aspect of mechanistic explanation, with special attention to neuroscience.

  4. For general discussions of systems biology, and associated philosophical issues, see Kitano (2002), O’Malley and Dupré (2005) and Boogerd et al. (2007).

  5. The landscape analogy is described at length in Organisers and Genes (1940), the frontispiece of which depicts ‘The Epigenetic Landscape’ as a river flowing through valleys to the sea. A shorter discussion, absent the term ‘epigenetic,’ appears in An Introduction to Modern Genetics (1939, 182–184). In Principles of Embryology (1956) and The Strategy of the Genes (1957) the model appears as a diagram.

  6. See Haraway (1976, esp. pp. 115–121).

  7. Cf. Waddington (1940): “One can compare a piece of developing tissue to a ball rolling down a system of valleys which branches downwards, like a delta… The tissue, like the ball…, must move downhill, but at some points there are two downhill paths open to it. At such branching points, it may sometimes require a definite external stimulus, such as an evocator substance, to push the tissue into one of the developmental paths…” (45; see also 92).

  8. See Gilbert (1991) for details.

  9. “Perhaps more important are the cases in which there are several fairly sharply demarcated and alternative developmental processes, which can only be represented by a system of branching lines. For instance, we have seen that in Drosophila there is a period of development when the normal vermilion substance is essential for normal eye pigmentation… In such a case we have a mixture of reacting substances, say two or three enzymes and some substrates, and at the branching point there are two alternative possible ways in which the mixture can change, according as the vermilion substance is present or not…” (1939, 182).

  10. See Bechtel (2010) for more on the pluralistic systems approach to cells.

  11. Some modeling frameworks in systems biology, notably GRNs, do retain Waddington’s idea that genes ultimately control development. But this is not an assumption of systems biology in general.

  12. In stochastic models, the state variable is usually molecule number, and the overall state of the system is not fully determined, but defined in terms of one or more probability distributions. For simplicity, this essay focuses on deterministic models, though the main points hold for stochastic systems models of cell development as well.

  13. MicroRNA is involved in a recently-discovered layer of gene regulation: short sequences of microRNA specifically bind mRNA transcripts and prevent their translation into protein. These inhibitory microRNAs have been coopted for experimental purposes, to probe the effects of specifically blocking translation of a particular gene (“knock-down,” a riff on “knock-out”).

  14. For details of each, see Alon (2007), Klipp et al. (2009), Szallasi et al. (2010) and Buchanan et al. (2010).

  15. In this particular model activation and inhibition of gene expression are represented as sigmoidal curves determined by Hill functions; n is the Hill coefficient and S the inflection point of the curve (details in Huang et al. 2007; Klipp et al. 2009). This modeling framework involves a number of approximations and idealizations, notably the omission of cooperative binding.

  16. The number and characteristics of steady-state solutions also depends on parameter values. Given a set of rate equations representing a molecular mechanism, however, these values are fixed. So this complication can be set aside for the purposes of this essay.

  17. There are many variants of this general cellular systems modeling approach. Of particular philosophical interest is that of Stuart Kauffman and colleagues (e.g., Huang et al. 2009), which involves further assumptions about the role of laws in life science, epistemic aims of modeling, and the relation of theory and experiment. These and other specific commitments of Kauffman’s research program will be examined in another paper.

  18. Though very early embryos lack distinct tissues, they do exhibit a ‘geography’ of body axes and inner/outer layers. The term ‘tissue location’ covers these cases as well.

  19. The timing of these first measurements also varies, from minutes after extraction, to months or even years later.

  20. The meanings of ‘totipotent’ and ‘pluripotent’ have shifted over time, and vary somewhat across biological fields. The usage specified here is standard in stem cell biology today, but this terminological consensus is quite recent.

  21. Though the term ‘reprogramming’ may evoke metaphors of genetic programs and information, these ideas play only a minor role in stem cell biology (Brandt 2010).

  22. These co-options of Waddington’s model occur in journals (Nature, Development, and Cell Stem Cell; see references to figures in this section), workshops (Ichida et al. 2010) and international conferences (J Thomson, 2010 ISSCR plenary address). Several other metaphors emphasizing dynamic and signal-mediated aspects of cell development have also appeared in similar contexts; e.g., a highway system (A. Wagers, personal communication), and pinball game (Sareen and Svendsen (2010). Thanks to an anonymous reviewer for Biology and Philosophy for bringing the latter to my attention.

  23. Waddington’s view of the matter is further brought out in his exchange with Thom in 1968 (166–179).

  24. Note that the source of the push is unspecified: it could be the experimenter, the specific TF proteins, the specific TF genes, or some combination thereof.

  25. Stability in models of cell development indicates whether a combination of gene expression levels can persist; it is not a thermodynamic concept.

  26. Fagan (in preparation). A closely related account of mechanistic explanation has been extensively defended for neuroscience (Craver 2007; Bechtel 2008), so the account assumed here is not idiosyncratic.

  27. In many cases, especially explanations of continuous processes, P is just M’s working. In such cases, the explanatory target is simply how M works, rather than how M works to bring about P.

  28. Note that in this case, the landscape model mediates between two different kinds of model: mathematical models and and model organisms. This interplay of models of different types makes the stem cell and systems biology case a particularly rich site for philosophical study.

References

  • Alon U (2007) An introduction to systems biology: design principles of biological circuits. Taylor and Francis, Boca Raton

    Google Scholar 

  • Bechtel W (2008) Mental mechanisms: philosophical perspectives on cognitive neuroscience. Routledge, New York

    Google Scholar 

  • Bechtel W (2010) The cell: locus or object of inquiry? Stud Hist Philos Biol Biomed Sci 41:172–182

    Google Scholar 

  • Bechtel W, Abrahamson A (2005) Explanation: a mechanist alternative. Stud Hist Philos Biol Biomed Sci 36:421–441

    Google Scholar 

  • Boogerd FC, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV (eds) (2007) Systems biology: philosophical foundations. Elsevier, Amsterdam

    Google Scholar 

  • Brandt C (2010) The metaphor of “nuclear reprogramming”: 1970s cloning research and beyond. In: Barahona A, Suarez-Díaz E, Rheinberger, H-J (eds) The hereditary hourglass: genetics and epigenetics, 1868–2000. Max Planck Institute for History of Science, pp 85–95

  • Buchanan M, Caldarelli G, de los Rios P, Rao F, Vendruscolo M (eds) (2010) Networks in cell biology. Cambridge University Press, Cambridge

    Google Scholar 

  • Conrad ED, Tyson JJ (2010) Modeling molecular interaction networks with nonlinear ordinary differential equations. In: Szallasi Z, Stelling J, Periwal V (eds) Systems modeling in cell biology: from concepts to nuts and bolts. The MIT Press, Cambridge, pp 97–123

    Google Scholar 

  • Craver C (2007) Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford University Press, Oxford

    Google Scholar 

  • de los Rios P, Vendruscolo M (2010) Network views of the cell. In: Buchanan M, Caldarelli G, de los Rios P, Rao F, Vendruscolo M (eds) Networks in cell biology. Cambridge University Press, Cambridge, pp 4–13

    Google Scholar 

  • Enver T, Pera M, Peterson C, Andrews P (2009) Stem cell states, fates, and the rules of attraction. Cell Stem Cell 4:387–397

    Article  Google Scholar 

  • Fagan MB (2011) Social experiments in stem cell biology. Perspect Sci 19:235–262

    Article  Google Scholar 

  • Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9:102–114

    Article  Google Scholar 

  • Gilbert S (1991) Epigenetic landscaping: Waddington’s use of cell fate bifurcation diagrams. Biol Philos 6:135–154

    Article  Google Scholar 

  • Glennan S (2002) Rethinking mechanistic explanation. Philos Sci 69:S342–S353

    Google Scholar 

  • Graf T, Enver T (2009) Forcing cells to change lineages. Nature 462:587–594

    Article  Google Scholar 

  • Haraway DJ (1976) Crystals, fabrics, and fields: metaphors that shape embryos. Yale University Press, New Haven

    Google Scholar 

  • Hochedlinger K, Plath K (2009) Epigenetic reprogramming and induced pluripotency. Development 136:509–523

    Article  Google Scholar 

  • Huang S (2009) Reprogramming cell fates: reconciling rarity with robustness. BioEssays 31:546–560

    Article  Google Scholar 

  • Huang S, Guo Y-P, May G, Enver T (2007) Bifurcation dynamics in lineage-commitment in biopotent progenitor cells. Dev Biol 305:695–713

    Article  Google Scholar 

  • Huang S, Ernberg I, Kauffman S (2009) Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Seminars Cell Dev Biol 20:869–876

    Article  Google Scholar 

  • Ichida J, Kiskinis E, Eggan K (2010) Shushing down the epigenetic landscape towards stem cell differentiation. Development 137:2455–2460

    Article  Google Scholar 

  • Jablonka E, Lamb M (2005) Evolution in four dimensions: genetic, epigenetic, behavioral, and symbolic variation in the history of life. The MIT Press, Cambridge

    Google Scholar 

  • Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664

    Article  Google Scholar 

  • Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H, Herwig R (2009) Systems biology: a textbook. Wiley-VCH, Weinheim

    Google Scholar 

  • Machamer P, Darden L, Craver C (2000) Thinking about mechanisms. Philos Sci 67:1–25

    Article  Google Scholar 

  • Maherali N, Hochedlinger K (2008) Guidelines and techniques for the generation of induced pluripotent stem cells. Cell Stem Cell 3:595–605

    Article  Google Scholar 

  • Noble D (2010) Biophysics and systems biology. Philos Trans R Soc A 368:1125–1139

    Article  Google Scholar 

  • O’Malley M, Dupré J (2005) Fundamental issues in systems biology. BioEssays 27:1270–1276

    Article  Google Scholar 

  • Pourquié O (2011) Steering a changing course. Development 138:1–2

    Article  Google Scholar 

  • Qu K, Ortoleva P (2008) Understanding stem cell differentiation through self-organization theory. J Theor Biol 250:606–620

    Article  Google Scholar 

  • Sareen D, Svendsen CN (2010) Stem cell biologists sure play a mean pinball. Nat Biotechnol 28:333–335

    Article  Google Scholar 

  • Szallasi Z, Stelling J, Periwal V (eds) (2010) Systems modeling in cell biology: from concepts to nuts and bolts. The MIT Press, Cambridge

    Google Scholar 

  • Takahashi S, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676

    Article  Google Scholar 

  • Trounson A (2009) Why stem cell research. In: Lanza R, Gearhart J, Hogan B, Melton D, Pederson R, Thomas ED, Thomson J, Wilmut I (eds) Essentials of stem cell biology, 2nd edn. Academic Press, San Diego, p xix

    Chapter  Google Scholar 

  • Van Speybrock L (2002) From epigenesis to epigenetics: the case of C. H. Waddington. Ann N Y Acad Sci 981:61–81

    Article  Google Scholar 

  • Waddington CH (1939) An introduction to modern genetics. MacMillan, New York

    Book  Google Scholar 

  • Waddington CH (1940) Organisers and genes. Cambridge University Press, Cambridge

    Google Scholar 

  • Waddington CH (1956) Principles of embryology. MacMillan, New York

    Book  Google Scholar 

  • Waddington CH (1957) The strategy of the genes. Taylor & Francis, London

    Google Scholar 

  • Waddington CH (ed) (1968) Towards a theoretical biology. IUBS and Edinburgh University Press, Edinburgh

    Google Scholar 

  • Yamanaka S (2009) Elite and stochastic models for induced pluripotent stem cell generation. Nature 460:49–52

    Article  Google Scholar 

  • Zhou Q, Melton DA (2008) Extreme makeover: converting one cell into another. Cell Stem Cell 3:382–388

    Article  Google Scholar 

Download references

Acknowledgments

Thanks to Amy Wagers, Irv Weissman, Oleg Igoshin, Elihu Gerson and two anonymous reviewers for Biology and Philosophy for discussion and comments. The paper has also benefited from comments by session participants at the 2010 meeting of &HPS3 (Indiana University), the Workshop on Modeling and Simulation (Pittsburgh, March 2011), the 2011 meeting of the Society for Philosophy of Science in Practice (University of Exeter), and the EFS Systems Biology Workshop at Aarhus University in August 2011. Funding for this research was generously provided by the Humanities Research Center at Rice University’s Collaborative Research Fellowship (2009–2010), and Faculty Innovation Fund (2010–2012).

Author information

Authors and Affiliations

  1. Department of Philosophy, Rice University, Houston, TX, USA

    Melinda Bonnie Fagan

Authors
  1. Melinda Bonnie Fagan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Melinda Bonnie Fagan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fagan, M.B. Waddington redux: models and explanation in stem cell and systems biology. Biol Philos 27, 179–213 (2012). https://doi.org/10.1007/s10539-011-9294-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10539-011-9294-y

Keywords

Search

Navigation