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Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks.

Oliveira AP, Patil KR, Nielsen J - BMC Syst Biol (2008)

Bottom Line: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated.Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions.Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, Building 223, DK-2800 Kgs, Lyngby, Denmark. apo@bio.dtu.dk

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.

Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes.

Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions. Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses. Our Reporter Features analyses of yeast glucose repression and human diabetes data brings hints towards the understanding of the principles of transcriptional regulation controlling these two important and potentially closely related systems.

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Summary of the different scoring systems suggested for Reporter analysis.
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Figure 6: Summary of the different scoring systems suggested for Reporter analysis.

Mentions: Identification of key responsive nodes in a biological interaction network relies on assigning a statistical score to each feature node. Such a score must be calculated in a biological meaningful way. Thus, we propose four different scoring systems that align with our biological hypothesis underlying the Reporter concept, which are depicted in Figure 6:


Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks.

Oliveira AP, Patil KR, Nielsen J - BMC Syst Biol (2008)

Summary of the different scoring systems suggested for Reporter analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2268660&req=5

Figure 6: Summary of the different scoring systems suggested for Reporter analysis.
Mentions: Identification of key responsive nodes in a biological interaction network relies on assigning a statistical score to each feature node. Such a score must be calculated in a biological meaningful way. Thus, we propose four different scoring systems that align with our biological hypothesis underlying the Reporter concept, which are depicted in Figure 6:

Bottom Line: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated.Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions.Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, Building 223, DK-2800 Kgs, Lyngby, Denmark. apo@bio.dtu.dk

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.

Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes.

Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions. Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses. Our Reporter Features analyses of yeast glucose repression and human diabetes data brings hints towards the understanding of the principles of transcriptional regulation controlling these two important and potentially closely related systems.

Show MeSH
Related in: MedlinePlus