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Design Under Randomness: How Variation Affects the Engineering of Biological Systems

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Abstract

Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with.

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Notes

  1. Though Giese et al. (2013) approach the question from the perspective of different groups of synthetic biologists, their categorization of rational and evolutionary groups is equivalent to the distinction between rational design and directed evolution design approaches.

  2. Directed evolution is framed as a more biologically inspired design method than rational design. However, one should not consider directed evolution as an exclusively biological design approach that is incompatible with engineering principles. Many engineering projects also rely on similar trial-and-error design methods (see Calcott et al. 2015), or evolutionarily based design, such as the use of genetic algorithms.

  3. A synchronic/diachronic distinction can also be discussed in relation to design goals. In his analysis of software engineering, Calcott (2014, p. 298) defines a synchronic goal as the attempt to “make the software do something useful now,” and a diachronic goal means to “make the software easy to modify” in the future. As Calcott notes, analysis of engineering tends to focus on synchronic goals, whereas diachronic aspects are overlooked.

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Acknowledgements

This research has been supported by the Academy of Finland research project “Biological Knowledge through Modeling and Engineering: Epistemological and Social Aspects of Synthetic Biology” (PI: Prof. Tarja Knuuttila, grant number 272604), Finnish Cultural Foundation and Finnish Centre of Excellence in the Philosophy of the Social Sciences. I am grateful to Alkistis Elliott-Graves, Rami Koskinen, Tarja Knuuttila, Jaakko Kuorikoski, Uskali Mäki, Jani Raerinne, Anita Välikangas, and referees for this journal who provided helpful comments on previous drafts of this paper.

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Correspondence to Tero Ijäs.

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Ijäs, T. Design Under Randomness: How Variation Affects the Engineering of Biological Systems. Biol Theory 13, 153–163 (2018). https://doi.org/10.1007/s13752-018-0294-x

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