Recent reads

I am doing some boring repetitive jobs that take some time to run (I am so glad to have a cluster to work with) and in the middle of the job runs I took some time to catch up to some paper reading. So here is some of the interesting stuff:

Number one goes to a provocative review/opinion from Fox Keller E. called "Revisiting 'scale-free' networks." There is a comment about it in Faculty 1000. The author talks about power law distributions in an historical perspective removing some of the exaggerated hype and maybe overly optimistic notion that the observations about scale free networks might contain some sort of "universal" truth about complex networks.

I talked before about the work of Rama Ranganathan when I went to a FEBS course on modular protein domains. I said that he had talked about PDZ domains but it was actually WW domains :). Anyway , what he talked about in the meeting was published in two papers in Nature. They are worth a look, specially as a good example of the combination of computational and experimental work. This work exemplifies what I consider a nice role for computational biology, to guide the experimental work. They suggest what are the necessary constraints for a protein fold and then they built them to test their folding and activity experimentally.

Small is beautiful ? I am interested in protein network evolution and this small report by Naama Barkai's group caught my eye. It is a very simple work, they show an example where a cis regulatory motif sequence was dropped during evolution in the Saccharomyces lineage in several genes. I usually like small interesting ideas demonstrated nicely but I dare to say that maybe this one is slightly to simple :).

There is also a paper that I disliked. The paper talks about "The binding properties ad evolution of homodimers in protein-protein interaction networks" but most the conclusions look obvious or misleading. They say for example that a protein that has self interactions has higher average number of neighbors than a random protein. The comparison is not fair because a protein that has self interactions, in their analysis, has two or more interactions (including the self interaction) and a random protein has one or more interactions. The fair comparison would be to compare homodimers with proteins in the network that have at least two interactions.