Source:http://linkedlifedata.com/resource/pubmed/id/16701391
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
6
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| pubmed:dateCreated |
2006-5-16
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| pubmed:abstractText |
Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:month |
Jun
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| pubmed:issn |
0169-5347
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
20
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
345-53
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| pubmed:dateRevised |
2011-5-3
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| pubmed:year |
2005
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| pubmed:articleTitle |
Network thinking in ecology and evolution.
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| pubmed:affiliation |
Center for Ecology and Evolutionary Biology, 5289 University of Oregon, Eugene, OR 97403-5289, USA. proulx@proulxresearch.org
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| pubmed:publicationType |
Journal Article
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