Duplication-divergence model of protein interaction network

(http://arxiv.org/abs/q-bio/0411052)
I like this paper a lot.

I like genetic algorithms (not as optimizers, but as models of evolution) but the usual version is Mendelian, in that there are a fixed number of alleles, and each allele has a fixed meaning. This is a genetic algorithm that is very simple, with general increase in complexity over time.

This reminds me of Koza's "Reverse Engineering and Automatic Synthesis of Metabolic Pathways from Observed Data Using Genetic Programming".

Koza's networks are metabolic, (proteins interacting with substrates, like sugars), while the duplication-divergence networks are of "protein-protein interactions" (I'm not sure what those consist of.)

The duplication-divergence paper is much more lightweight than Koza's, to it's credit. Koza has an agenda of demonstrating that genetic programming can solve practical problems, while the duplication-divergence paper is entirely about modeling real-life evolution.

I recommend this paper.