Simple Models / Complex Diseases

Series Conveners

Greg Radick (Philosophy, Religion and History of Science)

John Ladbury (Biology)

Colin Johnson (Medicine)

Ellen Clarke (Philosophy, Religion and History of Science)

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Series Overview

In biology, context matters.  A given DNA sequence can have very different effects in a body depending on genetic background and wider environment. “Disease genes” do not always cause disease; even when they do, symptoms and their severity may vary enormously.

Yet the stripping away of real-world variability and complexity remains a central – perhaps indispensable – strategy for scientists seeking to understand diseases. Deliberately simplified model systems are built and then investigated under the controlled conditions of the laboratory.

Simplification makes the mechanism of a disease tractable, yielding the replicable, publishable results on which scientific careers depend. But the process can easily introduce distortions that may limit the value of the knowledge gained, to the point where what is learned about is less the behaviour of real-world diseases than the behaviour of lab-bound disease models.

This seminar series will bring together members of the newly established Leeds Centre for Disease Models with members of the Centre for History and Philosophy of Science for cross-disciplinary reflections about how and why the simplicity/complexity balance gets struck in the ways that it does, where there might be room for improvement, and how historical and philosophical perspectives might help in identifying strategies for closing the gap between simple models and the complex world.

The series will culminate in a day-long, University-wide workshop, examining the general problem of simple models/complex realities as it arises across the disciplines.

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Research questions

(1)          How can scientists using model systems to investigate disease causes and cures better balance the virtues of simplification with the virtues of taking full account of real-world variability and complexity?

(2)          What role, if any, can integrating historians and philosophers of science into a scientific research culture play in helping scientists achieve this better balance, from the design of experiments to the development of therapies?

(3)          Do the answers to (1) and (2) apply straightforwardly to other disciplines where models matter, from epidemiology to economics, or does disease modelling involve distinctive problems, requiring distinctive solutions?