Linked Life Data

15189167

Source:http://linkedlifedata.com/resource/pubmed/id/15189167

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2004-6-10
pubmed:abstractText
One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0066-4154
pubmed:author
pubmed:issnType
Print
pubmed:volume
73
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1051-87
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Analyzing cellular biochemistry in terms of molecular networks.
pubmed:affiliation
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA. yuxia@csb.yale.edu
pubmed:publicationType
Journal Article, Comparative Study, In Vitro, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Review, Research Support, Non-U.S. Gov't