VFFVA: dynamic load balancing enables large-scale flux variability analysis
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VFFVA: dynamic load balancing enables large-scale flux variability analysis Marouen Ben Guebila Correspondence: [email protected] Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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: Metabolic models, Flux variability analysis, High performance computing, Systems biology
Background Constraint-based reconstruction and analysis (COBRA) methods enable the study of metabolic pathways in bacterial [1] and human [2] systems, in time and space [3]. The metabolic models are usually formulated as linear systems [4] that are often underdetermined [5], therefore several solutions could satisfy the subjected constraints. The set of alternate optimal solutions (AOS) describes the range of reaction rates that achieve the optimal objective such as biomass production. The AOS space is quantified using flux variability analysis (FVA) [5], which provides a range of minimum and maximum values for each variable of the system. FVA has been applied to find blocked reactions in the network [6], quantify the fitness of macrophages after the infection of Mycobacterium tuberculosis [7], resolve thermodynamically infeasible loops [8], and compute the essentiality of reactions [9].
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