2008
DOI: 10.1186/1752-0509-2-17
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Abstract: Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data.

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Cited by 119 publications
(137 citation statements)
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“…Genome-wide transcription analysis was performed, and the amylase-secreting strain (AAC) and the control strain (NC) were compared under aerobic and anaerobic conditions. In order to propose a putative final electron acceptor for the protein folding in the ER under anaerobic conditions, we combined our transcriptome data with a genome-scale metabolic model using the reporter metabolite algorithm (31,32) and identified the key metabolites around which significant transcriptional changes occurred. The top 15 reporter metabolites for each strain in the comparison of anaerobic and aerobic conditions were clustered in Fig.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Genome-wide transcription analysis was performed, and the amylase-secreting strain (AAC) and the control strain (NC) were compared under aerobic and anaerobic conditions. In order to propose a putative final electron acceptor for the protein folding in the ER under anaerobic conditions, we combined our transcriptome data with a genome-scale metabolic model using the reporter metabolite algorithm (31,32) and identified the key metabolites around which significant transcriptional changes occurred. The top 15 reporter metabolites for each strain in the comparison of anaerobic and aerobic conditions were clustered in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The FDR from the statistical analysis was used as input to the reporter features algorithm (31,32) to identify reporter metabolites, whose neighboring genes in the metabolic network were significantly changed between two conditions. The reporter analysis was performed using the Platform for Integrative Analysis of Omics Data package for R (33).…”
Section: Methodsmentioning
confidence: 99%
“…This led us to perform a more detailed analysis of the gene expression data. Using methods for integrative analysis 23,24 , we calculated enriched gene ontology (GO) terms for transcripts differing significantly between the two strains at both growth conditions, as well as for reporter metabolites 24 and reporter transcription factors 25 . These methods allow for identification of transcriptional hotspots in metabolic networks, that is, metabolites around which there are large transcriptional changes, and transcription factors (TFs) that drive key transcriptional responses.…”
Section: Resultsmentioning
confidence: 99%
“…The reporter metabolites, calculated using the algorithm of Patil and Nielsen 24 , indicate locations in the metabolism around which there are large transcriptional differences between the two strains. The reporter TFs, calculated using the algorithm of Oliveira et al 25 , indicate TFs for which there are significant changes in the expression of the genes they are controlling.…”
mentioning
confidence: 99%
“…This algorithm enables identification of so-called Reporter Metabolites (metabolites around which the most significant transcriptional changes occur), and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. In a later development (Oliveira et al, 2008), the authors extend the method for its use with other kinds of bio-molecular networks, to identify further key biological features (Reporter Features).…”
Section: Integration Of Omics Datasetsmentioning
confidence: 99%