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Regulatory network operations in the Pathway Tools software.

Paley SM, Latendresse M, Karp PD - BMC Bioinformatics (2012)

Bottom Line: We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators.We present algorithms for ranking the degree of regulatory influence of genes, and for computing the net positive and negative regulatory influences on a gene.Pathway Tools provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025, USA.

ABSTRACT

Background: Biologists are elucidating complex collections of genetic regulatory data for multiple organisms. Software is needed for such regulatory network data.

Results: The Pathway Tools software supports storage and manipulation of regulatory information through a variety of strategies. The Pathway Tools regulation ontology captures transcriptional and translational regulation, substrate-level regulation of enzyme activity, post-translational modifications, and regulatory pathways. Regulatory visualizations include a novel diagram that summarizes all regulatory influences on a gene; a transcription-unit diagram, and an interactive visualization of a full transcriptional regulatory network that can be painted with gene expression data to probe correlations between gene expression and regulatory mechanisms. We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators. We present algorithms for ranking the degree of regulatory influence of genes, and for computing the net positive and negative regulatory influences on a gene.

Conclusions: Pathway Tools provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network. Curated collections of regulatory data authored using Pathway Tools are available for Escherichia coli, Bacillus subtilis, and Shewanella oneidensis.

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Related in: MedlinePlus

A graph showing how the E. coli AroL protein is regulated by selected regulators.
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Figure 9: A graph showing how the E. coli AroL protein is regulated by selected regulators.

Mentions: Once the complete graph for a given protein has been constructed, it can be queried to generate a list of entities that either positively or negatively regulate the protein (the list may be incomplete because of regulators with conflicting influences). Given a particular regulator, the graph can be queried to determine the path(s) through which it influences the original entity. For example, Figure 9 shows a partial graph for the E. coli AroL protein. The top pane contains the full list of regulators whose effect is known for the specified depth limit. The bottom pane shows how the target is regulated by the selected regulators, with entities whose overall effect is positive shown in green, those whose overall effect is negative shown in red, and those whose effect is unknown shown in brown. The graph can be read from left to right. For example, expression of AroL is inhibited by both the the TrpR transcription factor bound to tryptophan and the TyrR transcription factor bound to tyrosine. Both of these transcription factors require their respective ligands for these activities, although the effect of TyrR without its ligand is ambiguous. Reactions that produce or consume tryptophan and tyrosine are then subject to further regulation.


Regulatory network operations in the Pathway Tools software.

Paley SM, Latendresse M, Karp PD - BMC Bioinformatics (2012)

A graph showing how the E. coli AroL protein is regulated by selected regulators.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473263&req=5

Figure 9: A graph showing how the E. coli AroL protein is regulated by selected regulators.
Mentions: Once the complete graph for a given protein has been constructed, it can be queried to generate a list of entities that either positively or negatively regulate the protein (the list may be incomplete because of regulators with conflicting influences). Given a particular regulator, the graph can be queried to determine the path(s) through which it influences the original entity. For example, Figure 9 shows a partial graph for the E. coli AroL protein. The top pane contains the full list of regulators whose effect is known for the specified depth limit. The bottom pane shows how the target is regulated by the selected regulators, with entities whose overall effect is positive shown in green, those whose overall effect is negative shown in red, and those whose effect is unknown shown in brown. The graph can be read from left to right. For example, expression of AroL is inhibited by both the the TrpR transcription factor bound to tryptophan and the TyrR transcription factor bound to tyrosine. Both of these transcription factors require their respective ligands for these activities, although the effect of TyrR without its ligand is ambiguous. Reactions that produce or consume tryptophan and tyrosine are then subject to further regulation.

Bottom Line: We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators.We present algorithms for ranking the degree of regulatory influence of genes, and for computing the net positive and negative regulatory influences on a gene.Pathway Tools provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025, USA.

ABSTRACT

Background: Biologists are elucidating complex collections of genetic regulatory data for multiple organisms. Software is needed for such regulatory network data.

Results: The Pathway Tools software supports storage and manipulation of regulatory information through a variety of strategies. The Pathway Tools regulation ontology captures transcriptional and translational regulation, substrate-level regulation of enzyme activity, post-translational modifications, and regulatory pathways. Regulatory visualizations include a novel diagram that summarizes all regulatory influences on a gene; a transcription-unit diagram, and an interactive visualization of a full transcriptional regulatory network that can be painted with gene expression data to probe correlations between gene expression and regulatory mechanisms. We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators. We present algorithms for ranking the degree of regulatory influence of genes, and for computing the net positive and negative regulatory influences on a gene.

Conclusions: Pathway Tools provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network. Curated collections of regulatory data authored using Pathway Tools are available for Escherichia coli, Bacillus subtilis, and Shewanella oneidensis.

Show MeSH
Related in: MedlinePlus