Publication Date:
2004-03-06
Description:
Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Milo, Ron -- Itzkovitz, Shalev -- Kashtan, Nadav -- Levitt, Reuven -- Shen-Orr, Shai -- Ayzenshtat, Inbal -- Sheffer, Michal -- Alon, Uri -- New York, N.Y. -- Science. 2004 Mar 5;303(5663):1538-42.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departments of Molecular Cell Biology, Physics of Complex Systems, and Computer Science, Weizmann Institute of Science, Rehovot 76100, Israel.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/15001784" target="_blank"〉PubMed〈/a〉
Keywords:
Animals
;
Biological Evolution
;
Caenorhabditis elegans/physiology
;
Drosophila melanogaster/genetics/growth & development
;
Feedback, Physiological
;
Humans
;
Internet
;
Language
;
Linguistics
;
Mathematics
;
*Models, Biological
;
*Models, Theoretical
;
Nerve Net/physiology
;
Probability
;
Proteins/chemistry
;
Sea Urchins/genetics/growth & development
;
Signal Transduction
;
Social Support
;
Synapses/*physiology
;
*Systems Theory
;
Transcription, Genetic
Print ISSN:
0036-8075
Electronic ISSN:
1095-9203
Topics:
Biology
,
Chemistry and Pharmacology
,
Computer Science
,
Medicine
,
Natural Sciences in General
,
Physics
