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dc.contributor.author
Töpfer, Nadine
dc.contributor.author
Jozefczuk, Szymon
dc.contributor.author
Nikoloski, Zoran
dc.date.accessioned
2019-04-24T13:06:01Z
dc.date.available
2017-06-10T14:43:31Z
dc.date.available
2019-04-24T13:06:01Z
dc.date.issued
2012
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/1752-0509-6-148
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/64536
dc.identifier.doi
10.3929/ethz-b-000064536
dc.description.abstract
Background Changes in environmental conditions require temporal effectuation of different metabolic pathways in order to maintain the organisms’ viability but also to enable the settling into newly arising conditions. While analyses of robustness in biological systems have resulted in the characterization of reactions that facilitate homeostasis, temporal adaptation-related processes and the role of cellular pathways in the metabolic response to changing conditions remain elusive. Results Here we develop a flux-based approach that allows the integration of time-resolved transcriptomics data with genome-scale metabolic networks. Our framework uses bilevel optimization to extract temporal minimal operating networks from a given large-scale metabolic model. The minimality of the extracted networks enables the computation of elementary flux modes for each time point, which are in turn used to characterize the transitional behavior of the network as well as of individual reactions. Application of the approach to the metabolic network of Escherichia coli in conjunction with time-series gene expression data from cold and heat stress results in two distinct time-resolved modes for reaction utilization—constantly active and temporally (de)activated reactions. These patterns contrast the processes for the maintenance of basic cellular functioning and those required for adaptation. They also allow the prediction of reactions involved in time- and stress-specific metabolic response and are verified with respect to existing experimental studies. Conclusions Altogether, our findings pinpoint the inherent relation between the systemic properties of robustness and adaptability arising from the interplay of metabolic network structure and changing environment.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Flux-based methods
en_US
dc.subject
Genome-scale metabolic network
en_US
dc.subject
Network optimization
en_US
dc.subject
Adaptation
en_US
dc.title
Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
dc.date.published
2012-11-30
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
6
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
148
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
005468370
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-10T14:45:47Z
ethz.source
ECIT
ethz.identifier.importid
imp5936506a7244196757
ethz.ecitpid
pub:102626
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-14T18:50:27Z
ethz.rosetta.lastUpdated
2019-04-24T13:06:12Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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