An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Arrieta-Ortiz, ML; Hafemeister, C; Bate, AR; Chu, T; Greenfield, A; Shuster, B; Barry, SN; Gallitto, M; Liu, B; Kacmarczyk, T; Santoriello, F; Chen, J; Rodrigues, CD; Sato, T; Rudner, DZ; Driks, A; Bonneau, R; Eichenberger, P

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Arrieta-Ortiz, ML Hafemeister, C Bate, AR Chu, T Greenfield, A Shuster, B Barry, SN Gallitto, M Liu, B Kacmarczyk, T Santoriello, F Chen, J Rodrigues, CD

Sato, T Rudner, DZ Driks, A Bonneau, R Eichenberger, P

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Publication Type:: Journal Article
Citation:: Molecular Systems Biology, 2015, 11 (11)
Issue Date:: 2015-11-01

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Field	Value	Language
dc.contributor.author	Arrieta-Ortiz, ML	en_US
dc.contributor.author	Hafemeister, C	en_US
dc.contributor.author	Bate, AR	en_US
dc.contributor.author	Chu, T	en_US
dc.contributor.author	Greenfield, A	en_US
dc.contributor.author	Shuster, B	en_US
dc.contributor.author	Barry, SN	en_US
dc.contributor.author	Gallitto, M	en_US
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Kacmarczyk, T	en_US
dc.contributor.author	Santoriello, F	en_US
dc.contributor.author	Chen, J	en_US
dc.contributor.author	Rodrigues, CD https://orcid.org/0000-0003-4541-0145	en_US
dc.contributor.author	Sato, T	en_US
dc.contributor.author	Rudner, DZ	en_US
dc.contributor.author	Driks, A	en_US
dc.contributor.author	Bonneau, R	en_US
dc.contributor.author	Eichenberger, P	en_US
dc.date.issued	2015-11-01	en_US
dc.identifier.citation	Molecular Systems Biology, 2015, 11 (11)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117240
dc.description.abstract	© 2015 The Authors. Published under the terms of the CC BY 4.0 license. Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.	en_US
dc.relation.ispartof	Molecular Systems Biology	en_US
dc.relation.isbasedon	10.15252/msb.20156236	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Bacillus subtilis	en_US
dc.subject.mesh	Spores, Bacterial	en_US
dc.subject.mesh	Systems Biology	en_US
dc.subject.mesh	Gene Expression Regulation, Bacterial	en_US
dc.subject.mesh	Genes, Bacterial	en_US
dc.subject.mesh	Models, Genetic	en_US
dc.subject.mesh	Databases, Genetic	en_US
dc.subject.mesh	Gene Regulatory Networks	en_US
dc.subject.mesh	Transcriptome	en_US
dc.title	An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.for	0601 Biochemistry and Cell Biology	en_US
utslib.for	0605 Microbiology	en_US
utslib.for	0699 Other Biological Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	open_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2015 The Authors. Published under the terms of the CC BY 4.0 license. Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

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http://hdl.handle.net/10453/117240