scholarly journals Comparative Genome-Scale Metabolic Reconstruction and Flux Balance Analysis of Multiple Staphylococcus aureus Genomes Identify Novel Antimicrobial Drug Targets

2009 ◽  
Vol 191 (12) ◽  
pp. 4015-4024 ◽  
Author(s):  
Deok-Sun Lee ◽  
Henry Burd ◽  
Jiangxia Liu ◽  
Eivind Almaas ◽  
Olaf Wiest ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Flux Balance Analysis ◽  
In Silico ◽  
Drug Targets ◽  
Gene Deletion ◽  
Multidrug Resistant ◽  
Antimicrobial Drug ◽  
Metabolic Reconstruction ◽  
Flux Balance ◽  
Balance Analysis

ABSTRACT Mortality due to multidrug-resistant Staphylococcus aureus infection is predicted to surpass that of human immunodeficiency virus/AIDS in the United States. Despite the various treatment options for S. aureus infections, it remains a major hospital- and community-acquired opportunistic pathogen. With the emergence of multidrug-resistant S. aureus strains, there is an urgent need for the discovery of new antimicrobial drug targets in the organism. To this end, we reconstructed the metabolic networks of multidrug-resistant S. aureus strains using genome annotation, functional-pathway analysis, and comparative genomic approaches, followed by flux balance analysis-based in silico single and double gene deletion experiments. We identified 70 single enzymes and 54 pairs of enzymes whose corresponding metabolic reactions are predicted to be unconditionally essential for growth. Of these, 44 single enzymes and 10 enzyme pairs proved to be common to all 13 S. aureus strains, including many that had not been previously identified as being essential for growth by gene deletion experiments in S. aureus. We thus conclude that metabolic reconstruction and in silico analyses of multiple strains of the same bacterial species provide a novel approach for potential antibiotic target identification.

Using Bees Hill Flux Balance Analysis (BHFBA) for in silico Microbial Strain Optimization

2013 ◽  
pp. 375-384 ◽  
Author(s):  
Yee Wen Choon ◽  
Mohd Saberi Bin Mohamad ◽  
Safaai Deris ◽  
Rosli Md. Illias ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Strain Optimization ◽  
Flux Balance ◽  
Balance Analysis ◽  
Microbial Strain

scholarly journals Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2097
Author(s):  
Supatcha Lertampaiporn ◽  
Jittisak Senachak ◽  
Wassana Taenkaew ◽  
Chiraphan Khannapho ◽  
Apiradee Hongsthong
Keyword(s):  
Flux Balance Analysis ◽  
Growth Temperature ◽  
In Silico ◽  
Critical Role ◽  
High Growth ◽  
Optimal Growth ◽  
Response Mechanism ◽  
Ppi Network ◽  
Flux Balance ◽  
Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains

2013 ◽  
Vol 37 (3) ◽  
pp. 521-532 ◽  
Author(s):  
Yee Wen Choon ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Rosli Md. Illias ◽  
Chuii Khim Chong ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Bees Algorithm ◽  
Flux Balance ◽  
Balance Analysis ◽  
Microbial Strains

IN SILICO GENE DELETION OF ESCHERICHIA COLI FOR OPTIMAL ETHANOL PRODUCTION USING A HYBRID ALGORITHM OF PARTICLE SWARM OPTIMIZATION AND FLUX BALANCE ANALYSIS

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Mei Jing Liew ◽  
Abdul Hakim Mohamed Salleh ◽  
Mohd Saberi Mohamad ◽  
Yee Wen Choon ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Flux Balance Analysis ◽  
Biomass Production ◽  
Hybrid Algorithm ◽  
Gene Deletion ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Gene Deletions ◽  
Flux Balance ◽  
Balance Analysis

Metabolic engineering of microorganism is widely used to enhance the production of metabolites that is useful in food additives, pharmaceutical, supplements, cosmetics, and polymer materials. One of the approaches for enhancing the biomass production is to utilize gene deletion strategies. Flux Balance Analysis is introduced to delete the gene that eventually leads the overproduction of the biomass and then to increase the biomass production. However, the result of biomass production obtained does not achieve the optimal production. Therefore, we proposed a hybrid algorithm of Particle Swarm Optimization and Flux Balance Analysis to attain an optimal gene deletion that is able to produce a higher biomass production. In this research, Particle Swarm Optimization is introduced as an optimization algorithm to obtain optimal gene deletions while Flux Balance Analysis is used to evaluate the fitness (biomass production or growth rate) of gene deletions. By performing an experiment on Escherichia coli, the results indicate that the proposed hybrid algorithm of Particle Swarm Optimization and Flux Balance Analysis is able to obtain optimal gene deletions that can produce the highest ethanol production. A hybrid algorithm is suggested due to its ability in seeking a higher ethanol production and growth rate than OptReg methods.

scholarly journals Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e102744 ◽  
Author(s):  
Yee Wen Choon ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Rosli Md. Illias ◽  
Chuii Khim Chong ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Flux Balance ◽  
Balance Analysis ◽  
Microbial Strains
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