DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia

Abstract

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. 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. 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.

Authors:
 [1];  [1];  [1]
  1. University of Luxembourg, Esch-sur-Alzette (Luxembourg). Luxembourg Centre for Systems Biomedicine
Publication Date:
Research Org.:
University of Luxembourg, Esch-sur-Alzette (Luxembourg)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1424909
Grant/Contract Number:  
SC0010429
Resource Type:
Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Volume: 33; Journal Issue: 9; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Heirendt, Laurent, Thiele, Ines, and Fleming, Ronan M. T. DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia. United States: N. p., 2017. Web. doi:10.1093/bioinformatics/btw838.
Heirendt, Laurent, Thiele, Ines, & Fleming, Ronan M. T. DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia. United States. https://doi.org/10.1093/bioinformatics/btw838
Heirendt, Laurent, Thiele, Ines, and Fleming, Ronan M. T. Mon . "DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia". United States. https://doi.org/10.1093/bioinformatics/btw838. https://www.osti.gov/servlets/purl/1424909.
@article{osti_1424909,
title = {DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia},
author = {Heirendt, Laurent and Thiele, Ines and Fleming, Ronan M. T.},
abstractNote = {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. 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. 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.},
doi = {10.1093/bioinformatics/btw838},
journal = {Bioinformatics},
number = 9,
volume = 33,
place = {United States},
year = {Mon Jan 16 00:00:00 EST 2017},
month = {Mon Jan 16 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 18 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Global reconstruction of the human metabolic network based on genomic and bibliomic data
journal, January 2007

  • Duarte, N. C.; Becker, S. A.; Jamshidi, N.
  • Proceedings of the National Academy of Sciences, Vol. 104, Issue 6
  • DOI: 10.1073/pnas.0610772104

COBRApy: COnstraints-Based Reconstruction and Analysis for Python
journal, January 2013

  • Ebrahim, Ali; Lerman, Joshua A.; Palsson, Bernhard O.
  • BMC Systems Biology, Vol. 7, Issue 1
  • DOI: 10.1186/1752-0509-7-74

Computationally efficient flux variability analysis
journal, September 2010


Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework
journal, February 2015


Computing in Operations Research Using Julia
journal, April 2015


Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide
journal, September 2010


Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
journal, August 2011

  • Schellenberger, Jan; Que, Richard; Fleming, Ronan M. T.
  • Nature Protocols, Vol. 6, Issue 9
  • DOI: 10.1038/nprot.2011.308

A community-driven global reconstruction of human metabolism
journal, March 2013

  • Thiele, Ines; Swainston, Neil; Fleming, Ronan M. T.
  • Nature Biotechnology, Vol. 31, Issue 5
  • DOI: 10.1038/nbt.2488

Works referencing / citing this record:

Dynamic load balancing enables large-scale flux variability analysis
posted_content, October 2018