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Unique identifier OMICS_29825
Name E-flux

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This tool is not maintained anymore.

Publication for E-flux

E-flux citations

 (52)
library_books

Network Analyses in Plant Pathogens

2018
Front Microbiol
PMCID: 5797656
PMID: 29441045
DOI: 10.3389/fmicb.2018.00035

[…] ity analysis was improved by the incorporation of the interaction in the modeling process.Other software tools can be used to model metabolic interactions between the host and the pathogen as are the E-flux (Colijn et al., ) or NetGenerator (Schulze et al., ) approaches. The E-Flux tool extends the genome-scale reconstructions and CBM approach, by integrating transcriptomic data into the model. Us […]

library_books

Staphylococcus aureus Responds to the Central Metabolite Pyruvate To Regulate Virulence

2018
MBio
PMCID: 5784258
PMID: 29362239
DOI: 10.1128/mBio.02272-17

[…] The E-Flux2 computational model was used to analyze the difference in intracellular metabolic fluxes between wild-type USA300 grown with (YCP) and wild-type USA300 grown without (YC) 2% pyruvate, as previ […]

library_books

Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

2018
Front Mol Biosci
PMCID: 5767226
PMID: 29376056
DOI: 10.3389/fmolb.2017.00096

[…] the user supplied expression threshold that might be unrealistic (Jensen and Papin, ). MADE decomposes gene expression data into a binary state and determines sets of low or highly active reactions. E-flux is a threshold based method that does not reduce the expression data into binary states, rather it converts the expression data to some suitable constraints that sets upper and lower limits to […]

library_books

Molecular characterization of breast cancer cell response to metabolic drugs

2018
Oncotarget
PMCID: 5839391
PMID: 29515760
DOI: 10.18632/oncotarget.24047

[…] tion proposed in Recon2 was used as an objective function representative of growth rate in tumor cells. Proteomics expression data were included in the model by solving GPR rules and using a modified E-flux algorithm []. Measuring GPR rule estimation values was performed using a variation of the method described by Barker et al. []. As described in previous works [], the mathematical operations us […]

library_books

Functional proteomics outlines the complexity of breast cancer molecular subtypes

2017
Sci Rep
PMCID: 5577137
PMID: 28855612
DOI: 10.1038/s41598-017-10493-w

[…] ms for “OR” expressions and minimums for “AND” expressions. Finally, the GPR rule values were normalized, dividing by the maximum value in each tumor, and were included in the Recon 2 model using the E-Flux algorithm. Normalized GPR rule values have been used to establish both lower and upper reaction bounds if the reaction is reversible. If the reaction is irreversible, low bound is set to 0 in a […]

library_books

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

2017
PLoS Comput Biol
PMCID: 5453602
PMID: 28520713
DOI: 10.1371/journal.pcbi.1005489

[…] o lower activity of the corresponding metabolic enzymes. Methods that impose condition-specific flux constraints on metabolic network models based upon gene expression data include GIMME [], iMAT [], E-Flux [], MADE [], GX-FBA [], MTA [], CoreReg [], mCADRE [] and EXAMO []. However, in many cases, the predictions obtained by FBA using a growth maximization objective are as good or better than thos […]

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E-flux institution(s)
Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Department of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Engineering Mathematics, University of Bristol, Bristol, UK; Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Phenomics and Bioinformatics Research Centre, School of Mathematics and Statistics, and Australian Centre for Plant Functional Genomics, University of South Australia, Mawson Lakes, SA, Australia; Department of Biomedical Engineering and Department of Microbiology, Boston University, Boston, MA, USA
E-flux funding source(s)
Supported in part with Federal funds from the National Institute of Allergy and Infectious Disease, National Institutes of Health Department of Health and Human Services, under Contract No. HHSN266200400001C; by NIAID 1U19AI076217; NIAID R01 071155; NIH HHSN 26620040000IC; NIH/NIAID Network for Large-Scale Sequencing of Microbial Genomes (014334-001); the Bill & Melinda Gates Foundation Dedicated Tuberculosis Gene Expression Database; the Ellison Medical Foundation ID-SS-0693-04 and by the Burroughs Wellcome Fund.

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