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Title: Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Journal Article · · Frontiers in Microbiology
 [1];  [2];  [2];  [3];  [4];  [2];  [5];  [6];  [6];  [7];  [8];  [9];  [10];  [11];  [12]
  1. Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Argonne, IL (United States). Computing, Environment and Life Sciences and Mathematics and Computer Science Division; Univ. of Minho, Braga (Portugal). Centre of Biological Engineering
  2. Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Argonne, IL (United States). Computing, Environment and Life Sciences
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Computational Biology and Bioinformatics Group
  4. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
  5. Univ. of Chicago, IL (United States). Computation Inst. and Dept. of Computer Science; Argonne National Lab. (ANL), Argonne, IL (United States). Computing, Environment and Life Sciences
  6. Univ. of Minho, Braga (Portugal). Centre of Biological Engineering
  7. Hope College, Holland, MI (United States). Biology Dept.
  8. Hope College, Holland, MI (United States). Computer Science Dept.
  9. Dordt College, Sioux Center, IA (United States). Dept. of Mathematics
  10. Argonne National Lab. (ANL), Argonne, IL (United States). Computing, Environment and Life Sciences; Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
  11. Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Argonne, IL (United States). Computing, Environment and Life Sciences; Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
  12. Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
+ Show Author Affiliations

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-means clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH). National Institute of Allergy and Infectious Diseases (NIAID); U. S. Department of Health and Human Services; National Science Foundation (NSF); Fundacao para a Ciencia ea Tecnologia of Portugal
Grant/Contract Number:
AC02-06CH11357; AC05-76RL01830
OSTI ID:
1372299
Alternate ID(s):
OSTI ID: 1339825
Report Number(s):
PNNL-SA-115054; 131302
Journal Information:
Frontiers in Microbiology, Vol. 7; ISSN 1664-302X
Publisher:
Frontiers Research FoundationCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

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Cited By (2)

KBase: The United States Department of Energy Systems Biology Knowledgebase journal July 2018
AGeNNT: annotation of enzyme families by means of refined neighborhood networks text January 2017

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Related Subjects

60 APPLIED LIFE SCIENCES
59 BASIC BIOLOGICAL SCIENCES
Atomic Regulon
CLR
Escherichia coli
clustering
gene expression analysis
hierarchical clustering
k-means clustering
transcriptomic data
atomic regulon