Linked Life Data

15319259

Source:http://linkedlifedata.com/resource/pubmed/id/15319259

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2005-1-12
pubmed:abstractText
Genetic regulatory networks are often affected by stochastic noise, due to the low number of molecules taking part in certain reactions. The networks can be simulated using stochastic techniques that model each reaction as a stochastic event. As models become increasingly large and sophisticated, however, the solution time can become excessive; particularly if one wishes to determine the effect on noise of changes to a series of parameters, or the model structure. Methods are therefore required to rapidly estimate stochastic noise.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
208-17
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
A method for estimating stochastic noise in large genetic regulatory networks.
pubmed:affiliation
Institute for Systems Biology 1441 North 34th Street, Seattle, WA 98103, USA. dorrell@systemsbiology.org
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies