Abstract
Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization (observed in a qualitative study of a systems biology laboratory) that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity.
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Notes
Elgin sometimes uses a different strategy that may amount to the same thing, using the notion of “metaphorical exemplification,” that is, non-literal exemplification. Thus, a lifeless painting can instantiate optimism, and a mathematical proof can instantiate elegance (Elgin 2002). The painting made of canvass and paint has no feelings, so it is not literally optimistic. But we agree that it’s an optimistic painting, so it instantiates optimism “metaphorically.” This argument depends on considerations about the difference between what is metaphorical and what is not, which we will not go into here.
Of course, new knowledge might be produced as well. For example, the diagram can provide warrant for claims about the existence of those new pathways in the model’s target system since (a) the diagram is counterfactually dependent on the computer model and (b) we have independent evidence that the model is accurate, so we can infer that this feature of the model is at least plausibly also instantiated in reality. But even in cases where no new knowledge is produced (e.g., there are no new pathways), we can still gain new understanding of the computational model through the diagram.
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Acknowledgements
We would like to thank the Center for Philosophy of Science of the University of Pittsburgh for funding while carrying out this research. Mike Stuart thanks the Social Sciences and Humanities Research Council of Canada for funding and the Centre for Philosophy of Natural and Social Science at the London School of Economics, and especially Roman Frigg, for support. We also thank the Lab Director and researchers in our study for welcoming us into their lab and granting us numerous interviews. For feedback we would like to thank Rami El Ali, Chiara Ambrosio, Agnes Bolinska, Hasok Chang, Johannes Lenhard, Josh Norton, Jacob Stegenga, Adam Toon, and two anonymous reviewers of this paper, as well as audiences at the Society for Philosophy of Science in Practice at the University of Ghent, the Lebanese American University, the UK Integrated HPS workshop in Nottingham, and the Imagination in Science Conference at the University of Leeds.
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Stuart, M.T., Nersessian, N.J. Peeking Inside the Black Box: A New Kind of Scientific Visualization. Minds & Machines 29, 87–107 (2019). https://doi.org/10.1007/s11023-018-9484-3
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DOI: https://doi.org/10.1007/s11023-018-9484-3