Scale Free Network In Biological Systems
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9.6 years ago
ugly.betty77 ★ 1.1k

Around 2000, Barabasi and Albert showed that many large-scale biological systems follow power-law network. Could anyone please tell me, what the current status of the field is? I used to follow those literature around 2000-2004 with great interest, but gave up afterward.

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Please put a link to the article you are citing and explain a bit what that means to provide some initial input to start the discussion. Personally, I do not enjoy 'what is the state of the field' questions that much, because they might require to write something like a review article. Maybe, if you meant to ask, 'could you please point me to some recent review articles in the field', this might become more acceptable.

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The original article must be the famous Emergence of Scaling in Random Networks article, plus some other published by Barabasi.

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Please put a link to the article you are citing and explain a bit what that means to provide some initial input to start the discussion.

Well, I gave the main result. As the above star mentioned, the original articles were quite famous among bioinformaticians in 2000-2003, and that is why I forgot to put a link. Istvan's answer covers a large subset.

I was asking for some recent review article and am checking to see whether the ideas got incorporated into other people's work outside Barabasi circle.

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9.6 years ago

Probably the paper listings at the Barabasi Lab are a good resource for finding out what has been going on in this field.

There is even the Review Article section: http://barabasilab.com/pubs-revart.php

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Thank you Istvan. I will take a look.

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9.6 years ago

I was also interested in this topic a few years ago, but I didn't see any interesting article recently.

The main problem is that the databases of protein-protein interactions on which Barabasi based his work were full of false positives, so in the end, it was too difficult to tell whether the results are valid or not. In particular, these annotations were built mainly using the Yeast-Two-Hybrids technique, which is known to give a lot of false results, and doesn't work for membrane proteins. In general, I think that the annotations for protein-protein interactions are still not good enough to give a definitive answer to whether these networks are scale-free, so, any result published now will be biased. Maybe we will know more in a few years :-)

If you want to know more, maybe you can sign up for the free online course on Systems Biology that is taking place these weeks at Coursera, and ask a question in the forum or to the lecturer. If you get an answer, please

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I wrote a couple of papers in early 2000s and thought the false positive problem was solved for good. However, nobody outside bioinformatics community believed those results. Especially it was hard to tell biologists that the results were from a physics-based statistical analysis and convince them to do experiment. I even went to the extent of going to yeast protein database (SGD) and annotated few unknown genes based on our analysis. Some of them are still not touched by any experimentalist !!

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I guess I may be a bit biased. I did a lot of work on database annotation, and I prone to think that the current status of the annotations is still incomplete. But I didn't read much literature on Systems Biology, so I am not the best person to criticize whether the key articles on this topic are technically valid or not.

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8.8 years ago

Another interesting paper from 2009 : The powerful law of the power law and other myths in network biology http://www.ncbi.nlm.nih.gov/pubmed/20023717

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8.8 years ago
jurgnjn ▴ 70

The following pair of essays offers a critical examination of some of the claims of (Barabási et al 1999), and an attempt at a rebuttal of the critique.

Keller EF. Revisiting 'scale-free' networks. Bioessays. 2005 Oct;27(10):1060-8. PubMed PMID: 16163729.

Norris V, Raine D. On the utility of scale-free networks. Bioessays. 2006 May;28(5):563-4. PubMed PMID: 16615092.

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8.8 years ago
Zag ▴ 10

Interesting to note that in the statistics community many of the "power-law" claims have been seriously criticised. I found the work by Cosma R. Shalizi very instructive. In a paper from 2007 he looked at many datasets claimed to exhibit power-law distribution, and find that the vast majority of them does not. They also look at the protein-protein interaction data from PNAS 2000 _Ito et al._ concluding that they don't come from a power-law distribution. You can also see these slides from Shalizi, especially slide 101.

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