Linked Life Data

19895901

Source:http://linkedlifedata.com/resource/pubmed/id/19895901

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-4-20
pubmed:abstractText
The majority of dynamic gene regulatory network (GRN) models are comprised of only a few genes and do not take multiple transcription regulation into account. The models are conceived in this way in order to minimize the number of kinetic parameters. In this paper, we propose a new approach for predicting kinetic parameters from DNA-binding site sequences by correlating the protein-DNA-binding affinities with nucleotide sequence conservation. We present the dynamic modeling of the cra modulon transcription in Escherichia coli during glucose-limited fed-batch cultivation. The concentration of the Cra regulator protein inhibitor, fructose 1,6-bis(phosphate), decreases sharply, eventually leading to the repression of transcription. Total RNA concentration data indicate a strong regulation of transcription through the availability of RNA polymerase. A critical assessment of the results of the model simulations supports this finding. This new approach for the prediction of transcription dynamics may improve the metabolic engineering of gene regulation processes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1096-7184
pubmed:author
pubmed:copyrightInfo
2009. Published by Elsevier Inc.
pubmed:issnType
Electronic
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
196-211
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Prediction of kinetic parameters from DNA-binding site sequences for modeling global transcription dynamics in Escherichia coli.
pubmed:affiliation
Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't