title: Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.
abstract: Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets,Atomic Regulons(ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs forEscherichia colibased on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions forE. colifound in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available forE. colito produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs forShewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, andStaphylococcus aureus, each of which represents increasing degrees of phylogenetic distance fromE. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Centre of Biological Engineering, University of Minho, Campus de GualtarBraga, Portugal; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA.
authors.forename: José P
authors.initials: JP
authors.lastname: Faria
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Davis
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA.
authors.forename: Janaka N
authors.initials: JN
authors.lastname: Edirisinghe
authors.affiliation: Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory (U.S. Dept. of Energy) Richland, WA, USA.
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Taylor
authors.affiliation: Mathematics and Computer Science Division, Argonne National Laboratory Argonne, IL, USA.
authors.forename: Pamela
authors.initials: P
authors.lastname: Weisenhorn
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA.
authors.forename: Robert D
authors.initials: RD
authors.lastname: Olson
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Department of Computer Science, Ryerson Physical Laboratory, University of ChicagoChicago, IL, USA.
authors.forename: Rick L
authors.initials: RL
authors.lastname: Stevens
authors.affiliation: Centre of Biological Engineering, University of Minho, Campus de Gualtar Braga, Portugal.
authors.forename: Miguel
authors.initials: M
authors.lastname: Rocha
authors.affiliation: Centre of Biological Engineering, University of Minho, Campus de Gualtar Braga, Portugal.
authors.forename: Isabel
authors.initials: I
authors.lastname: Rocha
authors.affiliation: Biology Department, Hope College Holland, MI, USA.
authors.forename: Aaron A
authors.initials: AA
authors.lastname: Best
authors.affiliation: Computer Science Department, Hope College Holland, MI, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: DeJongh
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA.
authors.forename: Nathan L
authors.initials: NL
authors.lastname: Tintle
authors.affiliation: Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Parrello
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA.
authors.forename: Ross
authors.initials: R
authors.lastname: Overbeek
authors.affiliation: Computation Institute, University of ChicagoChicago, IL, USA; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Henry
keywords: CLR; Escherichia coli; atomic regulon; clustering; gene expression analysis; hierarchical clustering; k-means clustering; transcriptomic data