Networks can be grown 'in silico'  typically by defining a set of probabilistic or optimization rules to sequentially add nodes and links. They are important because they allow:

• to understand how a real network could have been evolved (e.g. preferential attachment, limited resources, gene copying, ...),
• to quickly generate and classify a lot of different kinds of networks,
• to model the network under analysis with very few parameters and then try to predict new features.

Growing Networks can be analyzed by mean of 'balance' equation (rate or master equation, Krapivtsky, Dorogovtsev) focusing on the feature of interest, e.g. in and out degree (and joint) distribution, aging mechanism, loop distribution, ...

Moreover, often the obtained distributions can be successfully plugged into the Generating Function (Newman) formalism to quickly recover other important features, like e.g. average path length, giant cluster existence and size, appearance of some motifs or loops.

During our work, studying our Yeast Gene Network, we observed a peculiar degree distributions: while the out-degree distribution has a 'classical' power law (Barabasi), the in-degree distribution better fit to an exponential law. Then, we proposed a set of rules giving rise to a nice match with these features, susceptible of an immediate biological interpretation. Our results will soon be available.