Notes from the Biomass

The Chronicle of Higher Education features an article, titled Bloggers Need Not Apply, which is an interesting - and scary - read for the academic job seekers. I wonder whether the board, who was disappointed by the content of the blogs for one or the other reason would actually think that the applicants that did not blog were more capable and did not possess such flaws. Better the devil you know...

I've read a number of silly rants where young scientists commented on their PIs - doing that is stupid to begin with. Most science blogs that I read do not talk about much about the daily grind but reflect on the field in general, which I miss with many scientists that focus on the few angular seconds of their day sky.

A well kept blog will add to ones professional standing - certainly, there are a number of pitfalls one has to watch out for. Well, if I didn't, I should stop immediatly. Anyway, I might want to print the article and put it under my pillow.

[Via malorama]

The notion that many networks were scale free was an important discovery and relevant to all the people that studied protein interaction networks, obviously. Many people in my circle of peers, including me never liked the idea and always found it overrated.
Berend Snel and Martijn Huynen, then at the EMBL, noted that the scale does not really span the many orders of magnitude required to actually make the statement that the distribution is in fact independent of it. The most connected protein, JSN1 in yeast had 344 interactions (within a single data set), which can't compare to the millions of links one would find in the topology of the e.g. the internet.

Nevertheless, many bioinformaticians put the notion forward, and many articles included "scale free network" just because it sounded sexy.

Recently, a group around Marc Vidal published a simple, yet revealing experiment, to identify, whether we could acutally observe such properties given our incomplete and patchy sampling of protein-protein interaction networks.
They simply generate random networks, sample from them and were able to identify scale free properties in all of them.

The networks might be in fact scale free but I do not think that the notion will tell us much about biological networks, in particular as we cannot distiguish between sticky proteins that simply bind to a large number of proteins without transmitting any signal and methodological artifacts, both largely contributing to the proteins with many connections.