PMID- 24953453
OWN - NLM
STAT- MEDLINE
DCOM- 20150312
LR  - 20140623
IS  - 1613-4516 (Electronic)
IS  - 1613-4516 (Linking)
VI  - 11
IP  - 2
DP  - 2014 Jun 23
TI  - The topology of the growing human interactome data.
PG  - 238
LID - 10.2390/biecoll-jib-2014-238 [doi]
AB  - We have long moved past the one-gene–one-function concept originally 
      proposed by Beadle and Tatum back in 1941; but the full understanding of 
      genotype–phenotype relations still largely relies on the analysis of 
      static, snapshot-like, interaction data sets. Here, we look at what global 
      patterns can be uncovered if we simply trace back the human interactome network 
      over the last decade of protein- protein interaction (PPI) screening. We take a 
      purely topological approach and find that as the human interactome is getting 
      denser, it is not only gaining in structure (in terms of now being better fit by 
      structured network models than before), but also there are patterns in the way in 
      which it is growing: (a) newly added proteins tend to get linked to existing 
      proteins in the interactome that are not know to interact; and (b) new proteins 
      tend to link to already well connected proteins. Moreover, the alignment between 
      human and yeast interactomes spanning over 40% of yeast’s proteins — 
      that are involved in regulation of transcription, RNA splicing and other 
      cellcycle-related processes—suggests the existence of a part of the 
      interactome which remains topologically and functionally unaffected through 
      evolution. Furthermore, we find a small sub-network, specific to the 
      “core” of the human interactome and involved in regulation of 
      transcription and cancer development, whose wiring has not changed within the 
      human interactome over the last 10 years of interacome data acquisition. Finally, 
      we introduce a generalisation of the clustering coefficient of a network as a new 
      measure called the cycle coefficient, and use it to show that PPI networks of 
      human and model organisms are wired in a tight way which forbids the occurrence 
      large cycles.
FAU - Janjic, Vuk
AU  - Janjic V
AD  - Department of Computing, Imperial College London, London, SW7 2RH, United 
      Kingdom.
FAU - Przulj, Natasa
AU  - Przulj N
AD  - Department of Computing, Imperial College London, London, SW7 2RH, United 
      Kingdom.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20140623
PL  - Germany
TA  - J Integr Bioinform
JT  - Journal of integrative bioinformatics
JID - 101503361
SB  - IM
MH  - Algorithms
MH  - Animals
MH  - Arabidopsis/metabolism
MH  - Caenorhabditis elegans/metabolism
MH  - Cluster Analysis
MH  - Computational Biology/*methods
MH  - Databases, Protein
MH  - Drosophila melanogaster
MH  - Gene Expression Regulation
MH  - Humans
MH  - Neoplasms/metabolism
MH  - *Protein Interaction Mapping
MH  - Proteomics/*methods
MH  - Saccharomyces cerevisiae/metabolism
MH  - Species Specificity
MH  - Two-Hybrid System Techniques
EDAT- 2014/06/24 06:00
MHDA- 2015/03/13 06:00
CRDT- 2014/06/24 06:00
PHST- 2014/05/28 00:00 [received]
PHST- 2014/05/28 00:00 [revised]
PHST- 2014/06/23 00:00 [accepted]
PHST- 2014/06/24 06:00 [entrez]
PHST- 2014/06/24 06:00 [pubmed]
PHST- 2015/03/13 06:00 [medline]
AID - 793 [pii]
AID - 10.2390/biecoll-jib-2014-238 [doi]
PST - epublish
SO  - J Integr Bioinform. 2014 Jun 23;11(2):238. doi: 10.2390/biecoll-jib-2014-238.