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Systems Biology is changing the way biology is done. Is it a fad or is it effective? This blog tracks current happenings and helps you stay on top of the field. You can find a list of relevant papers at systems biology paper watch Have you heard a talk or read a paper in bioinformatics / systems biology you would like to tell other people about? Email: and get the word out!

Sunday, February 19, 2006

A simple physical model for scaling in protein-protein interaction networks. by Deeds, Ashenberg, Shakhnovich in Jan 2006 PNAS.

I've been sitting on this review for a few days because I wanted to really think about what the authors are saying in this paper and figure out the best way to convey it. What this paper posits is that the touted scale free property of protein-protein interaction networks may be misinformed. Scale free network theory says that the structure of the network is evolutionary favorable and allows for modularity, hierarchy, and redundancy on the molecular level. What Deeds et al. are saying is that this scale free topology can be achieved using a completely non evolutionary method.

As many in the field well know, protein-protein interaction networks are full of false positives, and there is little correlation between published studies. The authors shed light on the reason for this by using a hydrophobic model for protein interaction. Pretty much if you just take the proteins with the most hydrophobic exposed surface area and give them a higher probability to interact with each other (non-specifically and more importantly in a non-biological context) you can reconstruct the scale free nature of published yeast protein interaction networks.

So what does this mean? Well I think that to some extent this paper is getting at one of the true underlying sources of false positives in interaction networks. In fact, this model may lead to a good way to prune out non-specific interactions. What this paper does not say, and I agree with them, is that there is no biological information in these studies. It's true that alone the data may be noisy, but compiled with other sources of information including expression, function, and conservation, the data remain extremely useful.

tt: Biophysics, Networks, Scale Free


Anonymous Andreas Beyer said...

I suggest reading these previous papers, which deal with similar issues:

1. A nice paper regarding the hype around 'scale-free' networks:

2. A paper showing that you get different topologies for PPI with different experimental data:
(I.e., what kind of topology you get depends on the experimental method!)

3. A previous work by myself, showing that you can get other topologies with similar simple models as the one used by Deeds et al.:

6:44 AM  
Blogger Francis Crick said...

Thank you, Andreas Beyer, for your suggestions. We also have to do something about this site so that it doesn't cut off the urls.

3:21 PM  
Anonymous Trey Ideker said...

I mostly agree with your assessment earlier. The only thing I would add is that the group used the Ito data set to prove their points which is known to be the noisiest of all Y2H data sets out there.

1:30 PM  
Anonymous Andreas Beyer said...

Sorry for the long URL. I should have embedded them in HTML code ...

@Trey Ideker: Yes, but as we show in our papers, using TAP data we find an exponential distribution of node degrees. Hence, assuming a power-law maybe flawed in the first place. Let alone the biological interpretation of such distribution. So I am wondering if conclusions drawn from Peter Uetz's data would be more sensible. (Don't get me wrong: I am only speeking about the connectivity distribution! Peter's data are certainly valuable!)

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