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Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks.

Alvarez A, Woolf PJ - BMC Syst Biol (2011)

Bottom Line: Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations.Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. angelpr@umich.edu

ABSTRACT

Background: Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.

Results: Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using E. coli expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for E. coli was integrated with 266 E. coli gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.

Conclusion: POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.

Show MeSH
Optimization of genes grouped in the same operon. The networks in the top section are sub-networks of the original global network. Each sub-network has the original connectivity for the genes as suggested by RegulonDB and the target genes in each case form part of the same operon. In each case, each gene has an identical parent set. Note that in the three cases, the same non-predictive connections were predicted for each gene within each group.
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Figure 4: Optimization of genes grouped in the same operon. The networks in the top section are sub-networks of the original global network. Each sub-network has the original connectivity for the genes as suggested by RegulonDB and the target genes in each case form part of the same operon. In each case, each gene has an identical parent set. Note that in the three cases, the same non-predictive connections were predicted for each gene within each group.

Mentions: It was consistently observed that cases of genes in the same operon were disconnected from the same regulator or group of regulators after POBN optimization. In other cases none of the gene members of an operon were disconnected at all. See Figure 4 for some examples. This was expected to happen because, in most cases, genes in the same operon are regulated by the same group of regulators. It is interesting to notice that even when POBN visited each gene individually with no operon definition during the simulation, it was consistent in disconnecting (or not) all the member genes in an operon from the same regulators. Note that there can be few cases of complex operons that can transcribe subsets of genes by means of different promoters and internal transcriptional units within the same operon[22]. For example, in Figure 4.b the operon grouping the shown genes consists of three transcriptional units with three different promoters. That is why RegulonDB has the regulator IHF associated with only sucB and sucC and not with the rest of the members of the operon. After POBN, these two genes sucB and sucC were disconnected from IHF and also from the same regulators that the other members of the operon were disconnected (FNR and ArcA). This suggests that all the genes in this operon were transcribed by mean of a common promoter.


Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks.

Alvarez A, Woolf PJ - BMC Syst Biol (2011)

Optimization of genes grouped in the same operon. The networks in the top section are sub-networks of the original global network. Each sub-network has the original connectivity for the genes as suggested by RegulonDB and the target genes in each case form part of the same operon. In each case, each gene has an identical parent set. Note that in the three cases, the same non-predictive connections were predicted for each gene within each group.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117734&req=5

Figure 4: Optimization of genes grouped in the same operon. The networks in the top section are sub-networks of the original global network. Each sub-network has the original connectivity for the genes as suggested by RegulonDB and the target genes in each case form part of the same operon. In each case, each gene has an identical parent set. Note that in the three cases, the same non-predictive connections were predicted for each gene within each group.
Mentions: It was consistently observed that cases of genes in the same operon were disconnected from the same regulator or group of regulators after POBN optimization. In other cases none of the gene members of an operon were disconnected at all. See Figure 4 for some examples. This was expected to happen because, in most cases, genes in the same operon are regulated by the same group of regulators. It is interesting to notice that even when POBN visited each gene individually with no operon definition during the simulation, it was consistent in disconnecting (or not) all the member genes in an operon from the same regulators. Note that there can be few cases of complex operons that can transcribe subsets of genes by means of different promoters and internal transcriptional units within the same operon[22]. For example, in Figure 4.b the operon grouping the shown genes consists of three transcriptional units with three different promoters. That is why RegulonDB has the regulator IHF associated with only sucB and sucC and not with the rest of the members of the operon. After POBN, these two genes sucB and sucC were disconnected from IHF and also from the same regulators that the other members of the operon were disconnected (FNR and ArcA). This suggests that all the genes in this operon were transcribed by mean of a common promoter.

Bottom Line: Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations.Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. angelpr@umich.edu

ABSTRACT

Background: Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.

Results: Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using E. coli expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for E. coli was integrated with 266 E. coli gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.

Conclusion: POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.

Show MeSH