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

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 methodmore » 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.« less
Authors:
 [1] ;  [2] ;  [2] ;  [3] ;  [4] ;  [2] ;  [5] ;  [6] ;  [6] ;  [7] ;  [7] ;  [8] ;  [9] ;  [10] ;  [2]
  1. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Minho, Braga (Portugal)
  2. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
  5. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  6. Univ. of Minho, Braga (Portugal)
  7. Hope College, Holand, MI (United States)
  8. Dordt College, Sioux, IA (United States)
  9. Argonne National Lab. (ANL), Argonne, IL (United States); Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
  10. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
Publication Date:
Report Number(s):
PNNL-SA-115054
Journal ID: ISSN 1664-302X; KP1601010
Grant/Contract Number:
AC05-76RL01830; AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); 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
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; atomic regulon; clustering; gene expression analysis; transcriptomic data; Escherichia coli; hierarchical clustering; CLR; k-means clustering; 60 APPLIED LIFE SCIENCES
OSTI Identifier:
1372299
Alternate Identifier(s):
OSTI ID: 1339825