scholarly journals Assessment of Zymomonas mobilis biotechnological potential in ethanol production by flux variability analysis

2013 ◽  
Vol 3 (1) ◽  
pp. 1-5
Author(s):  
Agris Pentjuss ◽  
Uldis Kalnenieks
Keyword(s):  
Ethanol Production ◽  
Zymomonas Mobilis ◽  
Flux Variability Analysis ◽  
Biotechnological Potential ◽  
Variability Analysis ◽  
Flux Variability

scholarly journals Flux variability analysis approach of autism related metabolism in stoichiometric model of mitochondria

2013 ◽  
Vol 2 (2) ◽  
pp. 37-42 ◽  
Author(s):  
Agris Pentjuss ◽  
Oskars Rubenis ◽  
Daiga Bauze ◽  
Lilija Aprupe ◽  
Baiba Lace
Keyword(s):  
Analysis Approach ◽  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Stoichiometric Model ◽  
Flux Variability

scholarly journals Adaptive Flux Variability Analysis: A Tool To Deal With Uncertainties

2019 ◽  
Vol 52 (1) ◽  
pp. 70-75 ◽  
Author(s):  
T. Abbate ◽  
L. Dewasme ◽  
Ph. Bogaerts ◽  
A. Vande Wouwer
Keyword(s):  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Flux Variability

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

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Marouen Ben Guebila
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Parallel Implementation ◽  
Convergence Time ◽  
Coupled Models ◽  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Flux Variability ◽  
Fast Flux ◽  
Computation Load

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.

Adaptive flux variability analysis of HEK cell cultures

2020 ◽  
Vol 133 ◽  
pp. 106633 ◽  
Author(s):  
Thomas Abbate ◽  
Laurent Dewasme ◽  
Alain Vande Wouwer ◽  
Philippe Bogaerts
Keyword(s):  
Cell Cultures ◽  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Flux Variability
Sign in / Sign up

Export Citation Format

Share Document