Multiple Means of Determination and Multiple Constraints of Construction: Robustness and Strategies for Modeling Macromolecular Objects

Chapter
Part of the Boston Studies in the Philosophy of Science book series (BSPS, volume 292)

Abstract

The field of protein chemistry was transformed during the 1960s and 1970s by the development of theoretical and computational methods. In this chapter, I present and analyze, as a case study, one of these procedures. My aim is to describe how protein scientists have used and mutually adjusted limited resources in order to construct what became, for them, an efficient procedure of modeling protein structure. In order to specify the modeling strategy they have devised, as well as the characteristics of the models constructed, I discuss, first of all, the analytical framework proposed by Levins (in his 1966 paper “The Strategy of Model Building in Population Biology”), within which he analyzes modeling practices in population biology by delineating three strategies of modeling and introduces the concept of robustness analysis. I then describe the tension between the limitations protein scientists encountered, their aims for constructing models, and the complexity of proteinic objects. Next, I analyze precisely, by using Levins’ framework, the nature of the procedure of modeling by showing how the theoretical, empirical and computational limited resources used impact the nature of the protein models and are interrelated. I conclude by discussing Levins’ robustness analysis and the most general concept of robustness introduced by Wimsatt. I show that Levins’ analytical framework is an interesting tool for characterizing the modeling strategy used by protein scientists and for contrasting this strategy with the one Levins prefers as a population biology modeler. If the fruitfulness and efficiency of this last strategy is notably linked with robustness analysis, the fruitfulness of protein scientists’ modeling strategy is not associated with robustness analysis but with the stabilization of the modeling procedure, which cannot be described by using a general robustness scheme. I propose to consider that this procedure has acquired stability within a process of mutual and iterative adjustment of interrelated theoretical, empirical and computational constraints.

Keywords

Modeling Strategy Modeling Procedure Population Biology Robustness Analysis Modeling Practice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Many thanks to Léna Soler, Catherine Dufour, and Emiliano Trizio for helpful comments on previous drafts.

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Archives H. Poincaré, Laboratoire d’Histoire des Sciences et de PhilosophieUMR 7117 CNRSNancyFrance

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