Article (Scientific journals)
DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.
Heirendt, Laurent; Thiele, Ines; Fleming, Ronan MT
2017In Bioinformatics
Peer reviewed
 

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Keywords :
Computational modeling; Flux variability analysis; Metabolic networks; Parallel computing
Abstract :
[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.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Systems Biochemistry (Fleming Group)
Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group)
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Biochemistry, biophysics & molecular biology
Author, co-author :
Heirendt, Laurent  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Thiele, Ines ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Fleming, Ronan MT ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
External co-authors :
no
Language :
English
Title :
DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.
Publication date :
16 January 2017
Journal title :
Bioinformatics
Publisher :
Oxford University Press
Peer reviewed :
Peer reviewed
Focus Area :
Systems Biomedicine
Funders :
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
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since 28 March 2017

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