Biological context networks: a mosaic view of the interactome
Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depe...
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| Format: | Online |
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2006
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1693461/ |
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pubmed-16934612007-01-25 Biological context networks: a mosaic view of the interactome Rachlin, John Cohen, Dikla Dotan Cantor, Charles Kasif, Simon Article Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depend on temporal, spatial, or condition-specific contexts. In this paper, we introduce a novel graph construct called a biological context network that explicitly captures these changing patterns of interaction from one biological context to another. We consider known gene ontology biological process and cellular component annotations as a proxy for context, and show that aggregating small process-specific protein interaction sub-networks leads to the emergence of observed scale-free properties. The biological context model also provides the basis for characterizing proteins in terms of several context-specific measures, including ‘interactive promiscuity,' which identifies proteins whose interacting partners vary from one context to another. We show that such context-sensitive measures are significantly better predictors of knockout lethality than node degree, reaching better than 70% accuracy among the top scoring proteins. 2006-11-28 /pmc/articles/PMC1693461/ /pubmed/17130868 http://dx.doi.org/10.1038/msb4100103 Text en Copyright © 2006, EMBO and Nature Publishing Group |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Rachlin, John Cohen, Dikla Dotan Cantor, Charles Kasif, Simon |
| spellingShingle |
Rachlin, John Cohen, Dikla Dotan Cantor, Charles Kasif, Simon Biological context networks: a mosaic view of the interactome |
| author_facet |
Rachlin, John Cohen, Dikla Dotan Cantor, Charles Kasif, Simon |
| author_sort |
Rachlin, John |
| title |
Biological context networks: a mosaic view of the interactome |
| title_short |
Biological context networks: a mosaic view of the interactome |
| title_full |
Biological context networks: a mosaic view of the interactome |
| title_fullStr |
Biological context networks: a mosaic view of the interactome |
| title_full_unstemmed |
Biological context networks: a mosaic view of the interactome |
| title_sort |
biological context networks: a mosaic view of the interactome |
| description |
Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depend on temporal, spatial, or condition-specific contexts. In this paper, we introduce a novel graph construct called a biological context network that explicitly captures these changing patterns of interaction from one biological context to another. We consider known gene ontology biological process and cellular component annotations as a proxy for context, and show that aggregating small process-specific protein interaction sub-networks leads to the emergence of observed scale-free properties. The biological context model also provides the basis for characterizing proteins in terms of several context-specific measures, including ‘interactive promiscuity,' which identifies proteins whose interacting partners vary from one context to another. We show that such context-sensitive measures are significantly better predictors of knockout lethality than node degree, reaching better than 70% accuracy among the top scoring proteins. |
| publishDate |
2006 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1693461/ |
| _version_ |
1611391868162015232 |