Linked Life Data

12856234

Source:http://linkedlifedata.com/resource/pubmed/id/12856234

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2003-7-11
pubmed:abstractText
This article develops a theoretical framework to link dynamical and population genetic models of persistent viral infection. This linkage is useful because, while the dynamical and population genetic theories have developed independently, the biological processes they describe are completely interrelated. Parameters of the dynamical models are important determinants of evolutionary processes such as natural selection and genetic drift. We develop analytical methods, based on coupled differential equations and Markov chain theory, to predict the accumulation of genetic diversity within the viral population as a function of dynamical parameters. These methods are first applied to the standard model of viral dynamics and then generalized to consider the infection of multiple host cell types by the viral population. Each cell type is characterized by specific parameter values. Inclusion of multiple cell types increases the likelihood of persistent infection and can increase the amount of genetic diversity within the viral population. However, the overall rate of gene sequence evolution may actually be reduced.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0003-0147
pubmed:author
pubmed:issnType
Print
pubmed:volume
162
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
14-28
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Linking dynamical and population genetic models of persistent viral infection.
pubmed:affiliation
Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas 66045, USA. jkk@ku.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't