Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it>
<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whol...
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doaj-f893db694e6541cb94d0b58b516e26012020-11-25T00:24:17ZengBMCBMC Bioinformatics1471-21052007-05-018114910.1186/1471-2105-8-149Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it>Eils RolandZapatka MarcSchramm GunnarKönig Rainer<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.</p> <p>Results</p> <p>Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium <it>E. coli </it>to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.</p> http://www.biomedcentral.com/1471-2105/8/149 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Eils Roland Zapatka Marc Schramm Gunnar König Rainer |
spellingShingle |
Eils Roland Zapatka Marc Schramm Gunnar König Rainer Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> BMC Bioinformatics |
author_facet |
Eils Roland Zapatka Marc Schramm Gunnar König Rainer |
author_sort |
Eils Roland |
title |
Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> |
title_short |
Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> |
title_full |
Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> |
title_fullStr |
Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> |
title_full_unstemmed |
Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>Escherichia coli</it> |
title_sort |
using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of <it>escherichia coli</it> |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2007-05-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.</p> <p>Results</p> <p>Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium <it>E. coli </it>to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.</p> |
url |
http://www.biomedcentral.com/1471-2105/8/149 |
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