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Analyses Genome-scale metabolic network reconstructions (GENREs) and improves the predictive capabilities of draft GENREs by representing many alternative network structures. ensembleFBA can predict how small molecules interact with different essential genes in six Streptococcus species. The tool was tested by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. It correctly identifies many more essential genes in the model organism P. aeruginosa UCBPP-PA14 than the best individual GENREs.

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ensembleFBA specifications

Software type:
Package
Restrictions to use:
None
Operating system:
Unix/Linux
Computer skills:
Advanced
Interface:
Command line interface
Input data:
A Genome-scale metabolic network reconstruction, a set of both positive and negative growth conditions.
Programming languages:
MATLAB
Stability:
Stable
Source code URL:
https://github.com/mbi2gs/ensembleFBA

Credits

Publications

  • (Biggs et al., 2017) Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PloS Computational Biology.
    DOI: 10.1371/journal.pcbi.1005413

Institution(s)

Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA

Funding source(s)

Supported by grant 5R01GM108501 from the National Institute of General Medical Sciences.

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