scholarly journals In silico Analysis of 1,4-butanediol Heterologous Pathway Impact on Escherichia coli Metabolism

2019 ◽  
Vol 70 (10) ◽  
pp. 3448-3455
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Random Sampling ◽  
Solution Space ◽  
Production Potential ◽  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Flux Variability ◽  
Mutant Strains ◽  
The Impact

The bio-based synthesis of 1,4-butanediol (BDO), a key compound in many industries, has recently been achieved in Escherichia coli, however the yield even in glucose was far below the theoretical maximum. Furthermore, the impact of the BDO pathway on cell metabolism is yet to be discovered. The main objective of this study was to in silico improve and analyze the production potential of BDO on glucose and glycerol and evaluate the interaction between native and non-native pathways for wild-type and mutant strains using a simple biosynthetic pathway. The maximum production potential and changes in metabolic fluxes were simulated by different objective functions (biomass and BDO) and the reactions with highest differences were identified under different environmental conditions. Considering the outcomes 80% of the reactions with significant flux change were identical for all conditions simulated. Flux variability analysis was carried out to decipher the variation of fluxes and flux span changes (SC) were calculated. To further analyse the reactions with SC over 1 mmol gDW-1h-1 and to calculate the correlation coefficients for WT and mutant strains uniform random sampling was carried out. Most important variations in correlation patterns were observed for reactions in the mutant model. On the other hand, the addition, elimination and optimization of different pathways significantly affected the pairwise correlation patterns as well as the entire solution space of the network. Keywords: 1,4-butanediol, E. coli, flux variability analysis, random sampling, in silico, COBRA

scholarly journals In silico Analysis of 1,4-butanediol Heterologous Pathway Impact on Escherichia coli Metabolism

2019 ◽  
Vol 70 (10) ◽  
pp. 3448-3455
Author(s):  
Zsolt Bodor ◽  
Lehel Tompos ◽  
Aurelia Cristina Nechifor ◽  
Katalin Bodor
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Cell Metabolism ◽  
Solution Space ◽  
In Silico Analysis ◽  
Entire Solution ◽  
Production Potential ◽  
Pairwise Correlation ◽  
Mutant Strains ◽  
The Impact

The bio-based synthesis of 1,4-butanediol (BDO), a key compound in many industries, has recently been achieved in Escherichia coli, however the yield even in glucose was far below the theoretical maximum. Furthermore, the impact of the BDO pathway on cell metabolism is yet to be discovered. The main objective of this study was to in silico improve and analyze the production potential of BDO on glucose and glycerol and evaluate the interaction between native and non-native pathways for wild-type and mutant strains using a simple biosynthetic pathway. The maximum production potential and changes in metabolic fluxes were simulated by different objective functions (biomass and BDO) and the reactions with highest differences were identified under different environmental conditions. Considering the outcomes 80% of the reactions with significant flux change were identical for all conditions simulated. Flux variability analysis was carried out to decipher the variation of fluxes and flux span changes (SC) were calculated. To further analyse the reactions with SC over 1 mmol gDW-1h-1 and to calculate the correlation coefficients for WT and mutant strains uniform random sampling was carried out. Most important variations in correlation patterns were observed for reactions in the mutant model. On the other hand, the addition, elimination and optimization of different pathways significantly affected the pairwise correlation patterns as well as the entire solution space of the network.

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 Phycobilins as Potent Food Bioactive Broad-Spectrum Inhibitors Against Proteases of SARS-CoV-2 and Other Coronaviruses: A Preliminary Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Brahmaiah Pendyala ◽  
Ankit Patras ◽  
Chandravanu Dash
Keyword(s):  
Binding Affinity ◽  
Broad Spectrum ◽  
In Silico ◽  
Research Area ◽  
Therapeutic Drug ◽  
Inhibitor Activity ◽  
Current Vaccine ◽  
Mutant Strains ◽  
The Impact

In the 21st century, we have witnessed three coronavirus outbreaks: SARS in 2003, MERS in 2012, and the ongoing pandemic coronavirus disease 2019 (COVID-19). The search for efficient vaccines and development and repurposing of therapeutic drugs are the major approaches in the COVID-19 pandemic research area. There are concerns about the evolution of mutant strains (e.g., VUI – 202012/01, a mutant coronavirus in the United Kingdom), which can potentially reduce the impact of the current vaccine and therapeutic drug development trials. One promising approach to counter the mutant strains is the “development of effective broad-spectrum antiviral drugs” against coronaviruses. This study scientifically investigates potent food bioactive broad-spectrum antiviral compounds by targeting main protease (Mpro) and papain-like protease (PLpro) proteases of coronaviruses (CoVs) using in silico and in vitro approaches. The results reveal that phycocyanobilin (PCB) shows potential inhibitor activity against both proteases. PCB had the best binding affinity to Mpro and PLpro with IC50 values of 71 and 62 μm, respectively. Also, in silico studies with Mpro and PLpro enzymes of other human and animal CoVs indicate broad-spectrum inhibitor activity of the PCB. As with PCB, other phycobilins, such as phycourobilin (PUB), phycoerythrobilin (PEB), and phycoviolobilin (PVB) show similar binding affinity to SARS-CoV-2 Mpro and PLpro.

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.

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