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GEM-PRO specifications


Unique identifier OMICS_20289
Alternative name GEnome-scale Models with PROtein structures
Restrictions to use None
Community driven No
Data access Browse
User data submission Not allowed
Maintained Yes


  • Bacteria
    • Escherichia coli


  • person_outline Bernhard Palsson

Publication for GEnome-scale Models with PROtein structures

GEM-PRO citations


Multi omic data integration enables discovery of hidden biological regularities

Nat Commun
PMCID: 5095171
PMID: 27782110
DOI: 10.1038/ncomms13091

[…] Starting from the protein structures linked to metabolic genes in the GEM-PRO model, we annotated tertiary domains for each protein using the SCOP knowledgebase and FATCAT alignment tools. As a result of this analysis, the fraction of the protein aligning to an annotate […]


A Multi scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

PLoS Comput Biol
PMCID: 4965186
PMID: 27467583
DOI: 10.1371/journal.pcbi.1005039

[…] te version 4.4 was utilized for the construction of missing structures, and provides an especially useful method in modeling splice isoforms, which are specialized in the erythrocyte []. In the final GEM-PRO data frame, we note where available homology models have been mapped to their respective genes. We also include additional information in the data frame that explains the type of computational […]


Analysis of Genetic Variation and Potential Applications in Genome Scale Metabolic Modeling

Front Bioeng Biotechnol
PMCID: 4329917
PMID: 25763369
DOI: 10.3389/fbioe.2015.00013

[…] er of studies that use GSMs to systematically explore the effects of genetic variants on phenotypes. Chang et al. () conducted a study where GSMs coupled with protein structures of metabolic enzymes (GEM-PRO) were used to interpret genetic variant data of Escherichia coli strains evolved to tolerate high temperatures (Chang et al., ). In this study, a GSM of E. coli was constrained using experimen […]


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GEM-PRO institution(s)
Department of Bioengineering, University of California, La Jolla, San Diego, CA, USA; Joint BioEnergy Institute, Emeryville, CA, USA; Bioinformatics and Systems Biology Program, University of California, La Jolla, San Diego, CA, USA; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Office of the Director, National Institutes of Health, Bethesda, MD, USA
GEM-PRO funding source(s)
Supported by the Swiss National Science Foundation (grant p2elp2_148961), the Gordon and Betty Moore Foundation GBMF 2550.04 Life Sciences Research Foundation postdoctoral fellowship; the National Institutes of Health (grant GM057089); the Intramural Research Program of the National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health.

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