Heuristic approaches to models and modeling in systems biology
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Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control.
KeywordsSystems biology Prediction Control Heuristic Collaboration Parameters Mesoscopic modeling
This research was supported by a postdoctoral fellowship at the Academy Centre of Finland Excellence in the Philosophy of the Social Sciences, University of Helsinki. The ethnographic project was funded by the National Science Foundation (DRL097394084). I would like to thank the directors of Lab C and Lab G for welcoming us into the lab and the lab-members of those labs for granting us numerous interviews. I would like to thank in particular Nancy Nersessian for contributing many important insights involved in the development of this paper, as well as other members of our research group Vrishali Subramanhian, Lisa Osbeck, Sanjay Chandrasekharan, and Wendy Newstetter for their own useful insights. I would also like to thank Chiara Lisciandra for her thorough and helpful comments on an earlier version of this paper and two reviewers for their excellent advice on how to improve the paper.
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