Article ID: CBB645699525

Heuristic Approaches to Models and Modeling in Systems Biology (2015)


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.

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Authors & Contributors
MacLeod, Miles
Nersessian, Nancy J.
Greene, Mott T.
Creager, Angela N. H.
Müller, Gerd B.
Callebaut, Werner
History and Philosophy of the Life Sciences
Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences
Endeavour: Review of the Progress of Science
Perspectives on Science
Social Studies of Science
Annals of the History and Philosophy of Biology
Oxford University Press
Chronos Verlag
Troubador Publishing
Systems biology
Models and modeling in science
Forecasting; prediction
Data analysis
Philosophy of science
Time Periods
21st century
20th century
20th century, late
Himalayan Mountains (Nepal)
Human Genome Project

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