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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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Expression value comparisons between siblings. The expression of all the genes shown are associated by the same sigma factor sigma70. Panels (A) and (B) compare the expression values of genes that originally appeared associated only by the same sigma factor in the global regulatory network. The genes rspD and rspM shown in (A), are nearly linear related, while glyW and asnT shown in (B) have a more complex relationship. After POBN optimization, ompF gene was disconnected from 8 of its 9 originally regulators in the global regulatory network. Panels (C) and (D) illustrate two examples of the association of ompF with genes under this same single sigma factor sigma70. Note that different but clear non-linear associations are observed between ompF and its siblings.
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Figure 3: Expression value comparisons between siblings. The expression of all the genes shown are associated by the same sigma factor sigma70. Panels (A) and (B) compare the expression values of genes that originally appeared associated only by the same sigma factor in the global regulatory network. The genes rspD and rspM shown in (A), are nearly linear related, while glyW and asnT shown in (B) have a more complex relationship. After POBN optimization, ompF gene was disconnected from 8 of its 9 originally regulators in the global regulatory network. Panels (C) and (D) illustrate two examples of the association of ompF with genes under this same single sigma factor sigma70. Note that different but clear non-linear associations are observed between ompF and its siblings.

Mentions: An extreme example of regulator trimming took place for the gene ompF. This gene is associated with 9 regulators based on the RegulonDB database. After optimization, 8 regulatory connections were removed from ompF suggesting that, based on the data set under study, the expression of this gene is better explained by 1 regulator rather than 9. Upon examination of the siblings of ompF in the optimized network (e.g. the genes that are also regulated by the same single parent), we see a regular, but nonlinear relationship between the gene expression values of ompF and its siblings (see Figure 3).


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

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

Expression value comparisons between siblings. The expression of all the genes shown are associated by the same sigma factor sigma70. Panels (A) and (B) compare the expression values of genes that originally appeared associated only by the same sigma factor in the global regulatory network. The genes rspD and rspM shown in (A), are nearly linear related, while glyW and asnT shown in (B) have a more complex relationship. After POBN optimization, ompF gene was disconnected from 8 of its 9 originally regulators in the global regulatory network. Panels (C) and (D) illustrate two examples of the association of ompF with genes under this same single sigma factor sigma70. Note that different but clear non-linear associations are observed between ompF and its siblings.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Expression value comparisons between siblings. The expression of all the genes shown are associated by the same sigma factor sigma70. Panels (A) and (B) compare the expression values of genes that originally appeared associated only by the same sigma factor in the global regulatory network. The genes rspD and rspM shown in (A), are nearly linear related, while glyW and asnT shown in (B) have a more complex relationship. After POBN optimization, ompF gene was disconnected from 8 of its 9 originally regulators in the global regulatory network. Panels (C) and (D) illustrate two examples of the association of ompF with genes under this same single sigma factor sigma70. Note that different but clear non-linear associations are observed between ompF and its siblings.
Mentions: An extreme example of regulator trimming took place for the gene ompF. This gene is associated with 9 regulators based on the RegulonDB database. After optimization, 8 regulatory connections were removed from ompF suggesting that, based on the data set under study, the expression of this gene is better explained by 1 regulator rather than 9. Upon examination of the siblings of ompF in the optimized network (e.g. the genes that are also regulated by the same single parent), we see a regular, but nonlinear relationship between the gene expression values of ompF and its siblings (see Figure 3).

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