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Guebila MB. VFFVA: dynamic load balancing enables large-scale flux variability analysis. BMC Bioinformatics. 2020;21(1):424doi: 10.1186/s12859-020-03711-2.
Guebila, M. B. (2020). VFFVA: dynamic load balancing enables large-scale flux variability analysis. BMC bioinformatics, 21(1), 424. https://doi.org/10.1186/s12859-020-03711-2
Guebila, Marouen Ben. "VFFVA: dynamic load balancing enables large-scale flux variability analysis." BMC bioinformatics vol. 21,1 (2020): 424. doi: https://doi.org/10.1186/s12859-020-03711-2
Guebila MB. VFFVA: dynamic load balancing enables large-scale flux variability analysis. BMC Bioinformatics. 2020 Sep 29;21(1):424. doi: 10.1186/s12859-020-03711-2. PMID: 32993482; PMCID: PMC7523073.
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BMC Bioinformatics. 2020 Sep 29;21(1):424. doi: 10.1186/s12859-020-03711-2.

VFFVA: dynamic load balancing enables large-scale flux variability analysis.

BMC bioinformatics

Marouen Ben Guebila

Affiliations

  1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. [email protected].

PMID: 32993482 PMCID: PMC7523073 DOI: 10.1186/s12859-020-03711-2

Abstract

BACKGROUND: Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity.

RESULTS: Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage.

CONCLUSIONS: VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA .

Keywords: Flux variability analysis; High performance computing; Metabolic models; Systems biology

References

  1. EcoSal Plus. 2010 Sep;4(1): - PubMed
  2. Mol Syst Biol. 2007;3:121 - PubMed
  3. PLoS Comput Biol. 2010 Jul 15;6(7):e1000859 - PubMed
  4. BMC Syst Biol. 2008 Sep 16;2:79 - PubMed
  5. PLoS Comput Biol. 2009 Mar;5(3):e1000312 - PubMed
  6. BMC Bioinformatics. 2020 Sep 16;21(1):409 - PubMed
  7. BMC Bioinformatics. 2010 Sep 29;11:489 - PubMed
  8. Cell. 2015 May 21;161(5):971-987 - PubMed
  9. Mol Syst Biol. 2010 Oct 19;6:422 - PubMed
  10. Genome Res. 2004 Feb;14(2):301-12 - PubMed
  11. Bioinformatics. 2017 May 1;33(9):1421-1423 - PubMed
  12. Biophys J. 2010 May 19;98(10):2072-81 - PubMed
  13. Nat Protoc. 2019 Mar;14(3):639-702 - PubMed
  14. Mol Syst Biol. 2020 May;16(5):e8982 - PubMed
  15. Nat Biotechnol. 2013 May;31(5):419-25 - PubMed
  16. PLoS One. 2014 Feb 14;9(2):e86587 - PubMed
  17. Proc Natl Acad Sci U S A. 2020 Apr 14;117(15):8494-8502 - PubMed
  18. Bioinformatics. 2013 Apr 1;29(7):903-9 - PubMed
  19. Metab Eng. 2003 Oct;5(4):264-76 - PubMed
  20. Nat Biotechnol. 2010 Mar;28(3):245-8 - PubMed
  21. PLoS Comput Biol. 2018 Jul 5;14(7):e1006302 - PubMed
  22. J R Soc Interface. 2016 Nov;13(124): - PubMed

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