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Varieties of Noise: Analogical Reasoning in Synthetic Biology

Knuuttila, Tarja and Loettgers, Andrea (2013) Varieties of Noise: Analogical Reasoning in Synthetic Biology. [Preprint]

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Abstract

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Knuuttila, Tarjatarja.knuuttila@helsinki.fi
Loettgers, Andrealoettger@caltech.edu
Additional Information: To be published in Studies in History and Philosophy of Science (Part A)
Keywords: Synthetic biology, interdisciplinarity, analogical reasoning, engineering sciences, complex systems, noise
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
General Issues > Models and Idealization
General Issues > Technology
Depositing User: Tarja Tellervo Knuuttila
Date Deposited: 15 May 2013 20:27
Last Modified: 15 May 2013 20:27
Item ID: 9763
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
General Issues > Models and Idealization
General Issues > Technology
Date: January 2013
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/9763

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