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

Title:
VFFVA: dynamic load balancing enables large-scale flux variability analysis.
Authors:
Guebila MB; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. benguebila@hsph.harvard.edu.
Source:
BMC bioinformatics [BMC Bioinformatics] 2020 Sep 29; Vol. 21 (1), pp. 424. Date of Electronic Publication: 2020 Sep 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
User-Computer Interface*, Escherichia coli/metabolism ; Humans ; Metabolic Networks and Pathways ; Models, Biological ; Systems Biology/methods
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Contributed Indexing:
Keywords: Flux variability analysis; High performance computing; Metabolic models; Systems biology
Entry Date(s):
Date Created: 20200930 Date Completed: 20201027 Latest Revision: 20240802
Update Code:
20250114
PubMed Central ID:
PMC7523073
DOI:
10.1186/s12859-020-03711-2
PMID:
32993482
Database:
MEDLINE

Further Information

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 .