Complexity in genetic networks: topology vs. strength of interactions

Genetic regulatory networks are defined by their topology and by a multitude of continuously adjustable parameters. Here we present a class of simple models within which the relative importance of topology vs. interaction strengths becomes a well-posed problem. We find that complexity - the ability of the network to adopt multiple stable states - is dominated by the adjustable parameters. We comment on the implications for real networks and their evolution.

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