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EGRIN specifications


Unique identifier OMICS_21571
Alternative name Environment and Gene Regulatory Influence Network
Restrictions to use None
Community driven No
Data access File download
User data submission Not allowed
Version 2.0
Maintained Yes


  • person_outline David J. Reiss
  • person_outline Nitin Baliga

Publication for Environment and Gene Regulatory Influence Network

EGRIN citations


Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

PLoS Comput Biol
PMCID: 5453602
PMID: 28520713
DOI: 10.1371/journal.pcbi.1005489

[…] We expanded the yeast gene regulatory network derived using EGRIN and presented in [], in order to integrate it with PROM by focusing on predicting regulation for individual genes rather than for gene clusters as had been done previously. The yeast EGRIN was c […]


Mechanism for microbial population collapse in a fluctuating resource environment

Mol Syst Biol
PMCID: 5371734
PMID: 28320772
DOI: 10.15252/msb.20167058

[…] uences from 122 transcription factors and 12 environmental factors on 165 modules. Influence weight threshold of < −0.1 or > +0.1 was used to filter high confidence influences.Predictive power of the EGRIN model was tested using an expression data set that was not used in model construction (Bonneau et al, ), and by comparison with manually curated regulon members in the RegPrecise (Novichkov et a […]


Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis

Nat Microbiol
PMCID: 5010021
PMID: 27573104
DOI: 10.1038/nmicrobiol.2016.78

[…] The approach used in this study includes both computational and biological methods. Algorithms developed for the EGRIN model and the PROM model were implemented in the R programming language and MATLAB, respectively. Plots were generated using R, regulatory network diagrams generated using Cytoscape, and images […]


Data driven integration of genome scale regulatory and metabolic network models

Front Microbiol
PMCID: 4419725
PMID: 25999934
DOI: 10.3389/fmicb.2015.00409

[…] ions need to be considered. One possibility could involve leveraging a probabilistic formalism such as PROM for integrating inferred TRN models with ME-models. If such TRN models were developed using EGRIN or related approaches, environmental variables could also be integrated using PROM. Extension of ME-models with TRN information represents an exciting frontier that would provide a platform for […]


A Regulatory Hierarchy Controls the Dynamic Transcriptional Response to Extreme Oxidative Stress in Archaea

PLoS Genet
PMCID: 4287449
PMID: 25569531
DOI: 10.1371/journal.pgen.1004912

[…] the random normal distribution with the same mean, standard deviation, and number of samples in the actual data set (). All other p-values of significance listed in the text, including comparisons to EGRIN predictions, combinatorial control, arCOG functional enrichments, etc., were calculated using the hypergeometric test against the genome-wide background distribution unless indicated otherwise. […]


A high resolution network model for global gene regulation in Mycobacterium tuberculosis

Nucleic Acids Res
PMCID: 4191388
PMID: 25232098
DOI: 10.1093/nar/gku777

[…] The MTB EGRIN model was constructed from a compendium of 49 microarray datasets, containing 2327 publicly available transcriptional profiles for MTB genes (,). The microarray data were integrated with ∼250 00 […]


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EGRIN institution(s)
Institute for Systems Biology, Seattle, WA, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA; Département de Physique, de Génie Physique et d’Optique, Université Laval, Québec, QC, Canada; LabPIB, Department of Computing and Mathematics FFCLRP-USP, University of Sao Paulo, Ribeirao Preto, Brazil; Departments of Microbiology and Biology, University of Washington, Seattle, WA, USA; Lawrence Berkeley National Laboratories, Berkeley, CA, USA
EGRIN funding source(s)
Supported by the Office of Science, Office of Biological and Environmental Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; by grants from the U.S. Department of Energy (DE-FG02-04ER64685, DE-FG02-07ER64327, DE-FG02-08ER64685); the U.S. National Science Foundation (EAGER—MSB-1237267 and Interplay—NSF- 1330912—NSF-1262637); the U.S. National Institutes of Health, Center for Systems Biology (2P50GM076547); by the University of Luxembourg-ISB partnership; by the Department of Energy Office of Science Graduate Fellowship Program (DOE SCGF), made possible in part by the American Recovery and Reinvestment Act of 2009, and administered by ORISE-ORAU under contract no. DE-AC05- 06OR23100; and by São Paulo Research Foundation (FAPESP) grants 2012/05392-1 and 2011/08104-4.

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