eprintid: 1310
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/13/10
datestamp: 2012-07-03 12:13:49
lastmod: 2013-11-21 11:59:27
status_changed: 2012-07-03 12:13:49
type: article
metadata_visibility: show
creators_name: Penner, Orion
creators_name: Sood, Vishal
creators_name: Musso, Gabriel
creators_name: Baskerville, Kim
creators_name: Grassberger, Peter
creators_name: Paczuski, Maya
creators_id: orion.penner@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
title: Node similarity within subgraphs of protein interaction networks
ispublished: pub
subjects: QA
subjects: QH301
divisions: EIC
full_text_status: none
keywords: PACS: 87.14.Ee; 02.70.Uu; 87.10.+e; 89.75.Fb; 89.75.Hc
abstract: We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. The twinness of a pair of nodes is the number of connected, labeled subgraphs of size n in which the two nodes possess identical neighbours. The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). These include an Escherichia coli PIN and three Saccharomyces cerevisiae PINs -- each obtained using state-of-the-art high throughput methods. In almost all cases, the average twinness of node pairs is vastly higher than expected from a null model obtained by switching links. For all n, we observe a difference in the ratio of type A twins (which are unlinked pairs) to type B twins (which are linked pairs) distinguishing the prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is expected due to gene duplication, and whole genome duplication paralogues in S. cerevisiae have been reported to co-cluster into the same complexes. Indeed, we find that these paralogous proteins are over-represented as twins compared to pairs chosen at random. These results indicate that twinness can detect ancestral relationships from currently available PIN data. 
date: 2008-06
date_type: published
publication: Physica A: Statistical Mechanics and its Applications
volume: 387
number: 14
publisher: Elsevier
pagerange: 3801-3810
id_number: 10.1016/j.physa.2008.02.043
refereed: TRUE
issn: 0378-4371
official_url: http://www.sciencedirect.com/science/article/pii/S0378437108002379
related_url_url: http://arxiv.org/abs/0707.2076
citation:   Penner, Orion and Sood, Vishal and Musso, Gabriel and Baskerville, Kim and Grassberger, Peter and Paczuski, Maya  Node similarity within subgraphs of protein interaction networks.  Physica A: Statistical Mechanics and its Applications, 387 (14).  pp. 3801-3810.  ISSN 0378-4371      (2008)