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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.

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A partially observed bipartite network. A POBN model of a mechanism based on 2 unobserved regulators associated with 4 observed genes G1-G4.
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Figure 1: A partially observed bipartite network. A POBN model of a mechanism based on 2 unobserved regulators associated with 4 observed genes G1-G4.

Mentions: In this paper, we introduce a Bayesian network based method to differentiate predictive and non-predictive connections in a transcriptional regulatory network given a body of gene expression data. The method we term Partially Observed Bipartite Network, or POBN, uses a simplified Bayesian network topology to describe a regulatory network, as is illustrated in Figure 1. A POBN has a top layer of unobserved regulators (protein activities) that connect to a lower level of observed variables (mRNA expression values). By casting the regulators as unobserved, a POBN makes it explicit that the activities of the regulatory proteins are unknown. As a first approximation, the activity of a regulator could be modeled as simply proportional to the mRNA expression level of a transcription factor, however this approximation ignores other regulatory events that are known to influence the regulatory process. For example, the activity of a regulator may be influenced by post-translational modifications, changes in protein localization, sequestration, and/or cleavage--all of which are mediated by other pathways in the cell. Unfortunately, this more complete view of transcriptional factor activity is complex, poorly understood, and difficult to quantitatively model. To circumvent this problem, the POBN approach allows the expression of the target genes to dictate the likely activities of each regulator. In doing so, POBN strives to identify regulatory topologies that are maximally consistent with both the expression data and the known regulatory network, while not specifying the mechanistic details that lead to the particular state of the regulatory proteins.


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

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

A partially observed bipartite network. A POBN model of a mechanism based on 2 unobserved regulators associated with 4 observed genes G1-G4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A partially observed bipartite network. A POBN model of a mechanism based on 2 unobserved regulators associated with 4 observed genes G1-G4.
Mentions: In this paper, we introduce a Bayesian network based method to differentiate predictive and non-predictive connections in a transcriptional regulatory network given a body of gene expression data. The method we term Partially Observed Bipartite Network, or POBN, uses a simplified Bayesian network topology to describe a regulatory network, as is illustrated in Figure 1. A POBN has a top layer of unobserved regulators (protein activities) that connect to a lower level of observed variables (mRNA expression values). By casting the regulators as unobserved, a POBN makes it explicit that the activities of the regulatory proteins are unknown. As a first approximation, the activity of a regulator could be modeled as simply proportional to the mRNA expression level of a transcription factor, however this approximation ignores other regulatory events that are known to influence the regulatory process. For example, the activity of a regulator may be influenced by post-translational modifications, changes in protein localization, sequestration, and/or cleavage--all of which are mediated by other pathways in the cell. Unfortunately, this more complete view of transcriptional factor activity is complex, poorly understood, and difficult to quantitatively model. To circumvent this problem, the POBN approach allows the expression of the target genes to dictate the likely activities of each regulator. In doing so, POBN strives to identify regulatory topologies that are maximally consistent with both the expression data and the known regulatory network, while not specifying the mechanistic details that lead to the particular state of the regulatory proteins.

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