Reconstruction, verification and in-silico analysis of a genome-scale metabolic model of bacterial cellulose producing Komagataeibacter xylinus

2020 ◽  
Vol 43 (6) ◽  
pp. 1017-1026
Author(s):  
Mohammad Rezazadeh ◽  
Valiollah Babaeipour ◽  
Ehsan Motamedian
Keyword(s):  
Bacterial Cellulose ◽  
In Silico ◽  
In Silico Analysis ◽  
Metabolic Model ◽  
A Genome ◽  
Komagataeibacter Xylinus ◽  
Genome Scale ◽  
Silico Analysis

Reconstruction of a genome-scale metabolic model and in silico analysis of the polymalic acid producer Aureobasidium pullulans CCTCC M2012223

Gene ◽  
2017 ◽  
Vol 607 ◽  
pp. 1-8 ◽  
Author(s):  
Jun Feng ◽  
Jing Yang ◽  
Xiaorong Li ◽  
Meijin Guo ◽  
Bochu Wang ◽  
...  
Keyword(s):  
In Silico ◽  
Aureobasidium Pullulans ◽  
In Silico Analysis ◽  
Metabolic Model ◽  
Polymalic Acid ◽  
A Genome ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Reconstruction and in silico analysis of an Actinoplanes sp. SE50/110 genome-scale metabolic model for acarbose production

2015 ◽  
Vol 6 ◽  
Author(s):  
Yali Wang ◽  
Nan Xu ◽  
Chao Ye ◽  
Liming Liu ◽  
Zhongping Shi ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Metabolic Model ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

2020 ◽  
Vol 21 (4) ◽  
pp. 527-540 ◽  
Author(s):  
Lokanand Koduru ◽  
Hyang Yeon Kim ◽  
Meiyappan Lakshmanan ◽  
Bijayalaxmi Mohanty ◽  
Yi Qing Lee ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Leaf Blight ◽  
Xanthomonas Oryzae ◽  
Metabolic Reconstruction ◽  
Rice Leaf ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals In silico analysis of the fast-growing thermophile Geobacillus sp. LC300 using a novel genome-scale metabolic model

2021 ◽  
Author(s):  
Emil Ljungqvist ◽  
Martin Gustavsson
Keyword(s):  
Metabolic Engineering ◽  
Carbon Sources ◽  
In Silico Analysis ◽  
High Growth ◽  
Metabolic Model ◽  
Thermophilic Microorganisms ◽  
Cell Factories ◽  
Conversion Rates ◽  
A Genome ◽  
Genome Scale

AbstractThermophilic microorganisms show high potential for use as biorefinery cell factories. Their high growth temperatures provide fast conversion rates, lower risk of contaminations, and facilitated purification of volatile products. To date, only a few thermophilic species have been utilized for microbial production purposes, and the development of production strains is impeded by the lack of metabolic engineering tools. In this study, we constructed a genome-scale metabolic model, iGEL601, of Geobacillus sp. LC300, an important part of the metabolic engineering pipeline. The model contains 601 genes, 1240 reactions and 1305 metabolites, and the reaction reversibility is based on thermodynamics at the optimum growth temperature. Using flux sampling, the model shows high similarity to experimentally determined reaction fluxes with both glucose and xylose as sole carbon sources. Furthermore, the model predicts previously unidentified by-products, closing the gap in the carbon balance for both carbon sources. Finally, iGEL601 was used to suggest metabolic engineering strategies to maximise production of five industrially relevant compounds. The suggested strategies have previously been experimentally verified in other microorganisms, and predicted production rates are on par with or higher than those previously achieved experimentally. The results highlight the biotechnological potential of LC300 and the application of iGEL601 for use as a tool in the metabolic engineering workflow.

Genome-scale modeling and in silico analysis of mouse cell metabolic network

2009 ◽  
Vol 6 (1) ◽  
pp. 152-161 ◽  
Author(s):  
Suresh Selvarasu ◽  
Iftekhar A. Karimi ◽  
Ghi-Hoon Ghim ◽  
Dong-Yup Lee
Keyword(s):  
Metabolic Network ◽  
In Silico ◽  
In Silico Analysis ◽  
Mouse Cell ◽  
Scale Modeling ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Genome-scale reconstruction and in silico analysis of Klebsiella oxytoca for 2,3-butanediol production

2013 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Jong Myoung Park ◽  
Hyohak Song ◽  
Hee Jong Lee ◽  
Doyoung Seung
Keyword(s):  
In Silico ◽  
Klebsiella Oxytoca ◽  
In Silico Analysis ◽  
Genome Scale ◽  
Silico Analysis
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