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Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote
E. Strakova, A. Zikova, J. Vohradsky,
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2005
Free Medical Journals
od 1996
PubMed Central
od 1974
Europe PubMed Central
od 1974
Open Access Digital Library
od 1996-01-01 do 2030-12-31
Open Access Digital Library
od 1974-01-01
Open Access Digital Library
od 1996-01-01
Open Access Digital Library
od 1996-01-01
Medline Complete (EBSCOhost)
od 1996-01-01
Oxford Journals Open Access Collection
od 1996-01-01
ROAD: Directory of Open Access Scholarly Resources
od 1974
PubMed
24157841
DOI
10.1093/nar/gkt917
Knihovny.cz E-zdroje
- MeSH
- genetická transkripce MeSH
- genové regulační sítě * MeSH
- kinetika MeSH
- modely genetické * MeSH
- počítačová simulace MeSH
- regulace genové exprese u bakterií * MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
- sigma faktor metabolismus MeSH
- spory bakteriální genetika růst a vývoj metabolismus MeSH
- stanovení celkové genové exprese * MeSH
- Streptomyces coelicolor genetika metabolismus fyziologie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.
Citace poskytuje Crossref.org
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