NATURAL AND ARTIFICIAL COMPLEXITY

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