Recently I've started to become interested in network motifs and the surrounding issues. In particular, it seems difficult to be able to theoretically classify whether or not a certain frequency of a subgraph in a network constitutes a motif or not. With this in mind, I'm thinking about using random graphs to simulate the evolution of food webs, and examine whether or not motifs will appear in these simulations. This leads to the following question.
Question: What are some basic random graph models of the evolution of food webs (or networks in general)?
In particular, I would like to see speciation events (where nodes are created) and possibly extinction events (where nodes are deleted), thus the random graphs models that I'm more familiar with are unsuitable.
I cannot give you concrete examples as this is not my area of research - at the moment, but hopefully in the future. After attending the past two satellite conferences of RECOMB Systems Biology and Regulatory Genomics, I can suggest that you look to see what folks are doing in the worlds of cancer genomics and modeling of transcription pathways (the latter has good examples in modeling EGF receptor signaling, early Drosophila development and yeast transcription response to different stimuli). At those conferences, I never saw anyone present on food webs and that, in my mind, would make for a captivating talk.
Some names:
Drosophila - Mark Biggin;
Stem cell regulation - Richard Young;
Yeast - Nevan Krogan; ;
EGF signaling - Walter Fontana
Cancer genomics - Chris Sander and others at the MSKCC
Also, take a peek at what Ed Marcotte is doing with respect to disease models. There are several other names/labs of course.
I hope that this provides to you some info of use.
