In Silico Analysis for Biomass Synthesis under Different CO2 Levels for Chlamydomonas reinhardtii Utilizing a Flux Balance Analysis Approach

2014 ◽  
pp. 279-285 ◽  
Author(s):  
David Orlando Páez Melo ◽  
Rossmary Jay-Pang Moncada ◽  
Flavia Vischi Winck ◽  
Andrés Fernando González Barrios
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Flux Balance Analysis ◽  
In Silico ◽  
In Silico Analysis ◽  
Analysis Approach ◽  
Flux Balance ◽  
Co2 Levels ◽  
Balance Analysis ◽  
Silico Analysis

scholarly journals Metabolic engineering design to enhance (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis based on flux balance analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Nunthaphan Vikromvarasiri ◽  
Tomokazu Shirai ◽  
Akihiko Kondo
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Metabolic Pathways ◽  
In Silico Analysis ◽  
Metabolic Model ◽  
Fermentation Products ◽  
Flux Balance ◽  
Balance Analysis ◽  
Chemical Products ◽  
Genome Scale

Abstract Background Glycerol is a desirable alternative substrate for 2,3-butanediol (2,3-BD) production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis. Results B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study. Conclusions The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.

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.

scholarly journals Protein-x of hepatitis B virus in interaction with CCAAT/enhancer-binding protein α (C/EBPα) - an in silico analysis approach

2011 ◽  
Vol 8 (1) ◽  
pp. 41 ◽  
Author(s):  
Ashraf Mohamadkhani ◽  
Parisa Shahnazari ◽  
Zarrin Minuchehr ◽  
Armin Madadkar-Sobhani ◽  
Mahmoud Tehrani ◽  
...  
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
In Silico ◽  
Binding Protein ◽  
In Silico Analysis ◽  
Analysis Approach ◽  
Enhancer Binding Protein ◽  
B Virus ◽  
Protein X ◽  
Silico Analysis

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.

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
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