Reference : DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Engineering, computing & technology : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/30383
DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.
English
Heirendt, Laurent mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Thiele, Ines mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Fleming, Ronan MT mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
16-Jan-2017
Bioinformatics
Oxford University Press
Yes
[en] Computational modeling ; Flux variability analysis ; Metabolic networks ; Parallel computing
[en] MOTIVATION:
Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.
RESULTS:
DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.
AVAILABILITY:
The code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl.
Luxembourg Centre for Systems Biomedicine (LCSB): Systems Biochemistry (Fleming Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group)
National Centre of Excellence in Research (NCER) on Parkinson’s disease ; Luxembourg National Research Fund (FNR) ATTRACT program grant (FNR/A12/01) ; U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant no. DE-SC0010429
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/30383
10.1093/bioinformatics/btw838
http://www.ncbi.nlm.nih.gov/pubmed/28093405

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