Performing abstraction: two ways of modelling Arabidopsis thaliana

Abstract

What is the best way to analyse abstraction in scientific modelling? I propose to focus on abstracting as an epistemic activity, which is achieved in different ways and for different purposes depending on the actual circumstances of modelling and the features of the models in question. This is in contrast to a more conventional use of the term ‘abstract’ as an attribute of models, which I characterise as black-boxing the ways in which abstraction is performed and to which epistemological advantage. I exemplify my claims through a detailed reconstruction of the practices involved in creating two types of models of the flowering plant Arabidopsis thaliana, currently the best-known model organism in plant biology. This leads me to distinguish between two types of abstraction processes: the ‘material abstracting’ required in the production of Arabidopsis specimens and the ‘intellectual abstracting’ characterising the elaboration of visual models of Arabidopsis genomics. Reflecting on the differences between these types of abstracting helps to pin down the epistemic skills and research commitments used by researchers to produce each model, thus clarifying how models are handled by researchers and with which epistemological implications.

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Notes

  1. 1.

    See the collections of essays edited by Morgan and Morrison (1999), de Chadarevian and Hopwood (2004) and Laubichler and Muller (2007) for examples of how different types of models are combined in scientific research. Note that it is not within the scope of this paper to provide an innovative definition of what a ‘model’ is: the broadly defined notion of models as mediators, put forward by Morgan and Morrison (1999), suffices for my purposes.

  2. 2.

    Griesemer (2004, p. 436).

  3. 3.

    This estimate is based on data collected by The Arabidopsis Information Resource in 2006.

  4. 4.

    The history of research on Arabidopsis and its progressive institutionalisation (including the establishment of the Multinational Arabidopsis Steering Committee) is documented in Somerville and Koornneef (2002), Meyerowitz (2001), Meinke et al. (1998) and Leonelli (2007).

  5. 5.

    I visited The Arabidopsis Information Centre in August 2004 and the Nottingham Arabidopsis Stock Centre in May 2005. At both centres, Directors Sue Rhee and Sean May provided me with access to their archives and resources. I also had the possibility to interview them and their staff at length, thus gathering information about how they construct and use the models.

  6. 6.

    Another two sites are the Munich Information Centre for Protein Sequences [MIPS] and the ‘Arabidopsis.info’ based at the Nottingham Arabidopsis Stock Centre [NASC].

  7. 7.

    The initial focus on genomics was determined by the abundance of data gathered through the Arabidopsis Genome Initiative (the multinational project that successfully sequenced the plant’s genome between 1996 and 2000). TAIR personnel insists that the choice to organise the database on the basis of genomic data is pragmatic rather than conceptual, and that the ultimate TAIR aim is to obtain balance and integration of information pertaining to the genomic, the evolutionary and the ecological levels (pers. comm.). Nevertheless, as I show, the commitment to emphasise gene-level data prevents TAIR from giving equal space to data concerning higher levels of organisation in Arabidopsis.

  8. 8.

    A model of metabolic cycle is displayed in Fig. 5. See TAIR website for further examples.

  9. 9.

    More information on this project can be found online: http://obo.sourceforge.net/

  10. 10.

    The definitions used for the concepts employed in GO are agreed upon during GO Content Meetings, in which developers discuss their choices with experts in relevant biological domains.

  11. 11.

    Note that ‘schema’ in TAIR terminology does not denote the organisation of data into various categories (which occurs in steps 1–3), but rather the way in which programmers visualise these categories through available digital technologies.

  12. 12.

    My list is not supposed to be exhaustive, but rather to give an idea of the confusion underlying the use of the term ‘abstract’ in discussions of modelling practices.

  13. 13.

    See Cat (2001) for an exploration of this notion of abstraction in relation to Maxwell’s work.

  14. 14.

    A good exemplification of this view can be found in Cartwright (1999): a description is abstract insofar as it can be ‘fitted out’ to a number of other descriptions. Similarly, the characterisation of a model as abstract or concrete depends on the context in which the model is used. This approach is captured by Radder’s definition of abstraction as ‘summarising’, which he identifies (and goes on to criticise) as one of three main senses in which abstraction works (2006: 110).

  15. 15.

    The use of intellectually abstracted models is increasingly widespread among biologists. Take the pervasive use of simulations and algorithms to visualise empirical data, not to mention the push towards formalisation and away from the laboratory brought about by the increasing use of bioinformatics to store, organise and integrate data. These models are especially useful for elaborating explanations or confirming predictions stemming from given hypotheses (what Cartwright calls interpretative models in her 1999, p. 181). They are also fundamental to the integration of biological knowledge concerning specific phenomena (as, for instance, bringing together insights from physiology, genomics and cell biology to understand root development in plants). However, precisely because of their strict reliance on theoretical assumptions, models constructed through intellectual abstracting are not very helpful in cases where the goal of their manipulation is to improve the empirical content of a theory. They give little indication as to which features of the phenomenon under scrutiny should be considered relevant to the development of explanatory knowledge about that phenomenon. Further, such models do not help with testing the empirical (descriptive) accuracy of the relation it stipulates between theoretical terms and aspects of the phenomenon.

  16. 16.

    See Clarke and Fujimura (1992).

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Internet resources

  1. The Multinational Coordinated Arabidopsis thaliana Functional Genomics Project. Annual Report (2004) http://www.arabidopsis.info/info/masc_june_04.pdf

  2. NASC http://www.arabidopsis.info

  3. Open Biological Ontologies http://www.obo.souceforge.net

  4. TAIR http://www.arabidopsis.org

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Acknowledgments

Discussions with Rachel Ankeny, Hasok Chang, James Griesemer, Henk de Regt and Hans Radder were crucial to the development of my analysis. Thomas Reydon, Rasmus Winther, an anonymous reviewer and the editor closely read and commented upon the last draft, which has considerably improved as a result. I also thank the Arabidopsis researchers who shared their time, facilities and thoughts with me: Sue Rhee and her team at the TAIR and Sean May and his team at the NASC. This research was supported by the Netherlands Organisation for Scientific Research (NWO), The Leverhulme Trust and the ESRC.

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Correspondence to Sabina Leonelli.

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Leonelli, S. Performing abstraction: two ways of modelling Arabidopsis thaliana . Biol Philos 23, 509–528 (2008). https://doi.org/10.1007/s10539-007-9081-y

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Keywords

  • Abstraction
  • Arabidopsis thaliana
  • Commitments
  • Modelling
  • Model organisms
  • Skills
  • Understanding