  We present a network model in which words over a specific alphabet, called
{\it structures}, are associated to each node and undirected edges are added
depending on some distance between different structures. It is shown that this
model can generate, without the use of preferential attachment or any other
heuristic, networks with topological features similar to biological networks:
power law degree distribution, clustering coefficient independent from the
network size, etc. Specific biological networks ({\it C. Elegans} neural
network and {\it E. Coli} protein-protein interaction network) are replicated
using this model.
