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Natural and Artificial Complexity

Published online by Cambridge University Press:  01 April 2022

Robert C. Richardson
Robert C. Richardson*
Affiliation:
University of Cincinnati
*
†Department of Philosophy, University of Cincinnati, Cincinnati OH 45221–0374.
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Abstract

Genetic regulatory networks are complex, involving tens or hundreds of genes and scores of proteins with varying dependencies and organizations. This invites the application of artificial techniques in coming to understand natural complexity. I describe two attempts to deploy artificial models in understanding natural complexity. The first abstracts from empirically established patterns, favoring random architectures and very general constraints, in an attempt to model developmental phenomena. The second incorporates detailed information concerning the genetic structure, organization, and dependencies in actual systems in an attempt to explain developmental differences. The results offered by these models, pitched at these different levels of abstraction, are different. The more detailed models are more continuous with classical developmental approaches.

Information

Type
Symposium: Complexity and Experimentation in Molecular Biology
Information
Philosophy of Science , Volume 64 , Issue S4: Supplement. Proceedings of the 1996 Biennial Meetings of the Philosophy of Science Association. Part II: Symposia Papers Dec., 1997 , 1997, pp. S255 - S267
Copyright
Copyright © Philosophy of Science Association 1997

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Footnotes

‡

This work was supported by the Taft Faculty Committee at the University of Cincinnati. I am indebted to Richard Burian and Miriam Solomon for input. Lindley Darden provided not only useful comments for this work, but suggestions which would lead beyond it. I am indebted here for her reflections, and in future work as well.

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