scholarly journals In‐silico media optimization for continuous cultures using genome scale metabolic networks: the case of CHO‐K1

2021 ◽  
Author(s):  
Bárbara Ariane Pérez‐Fernández ◽  
Jorge Fernández‐de‐Cossio‐Dı́az ◽  
Tammy Boggiano ◽  
Kalet León ◽  
Roberto Mulet
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Media Optimization ◽  
Continuous Cultures ◽  
Genome Scale

scholarly journals In-silico media optimization for continuous cultures using genome scale metabolic networks: the case of CHO-K1

2020 ◽  
Author(s):  
Barbara Ariane P rez Fern ndez ◽  
Jorge Fernandez de Cossio Diaz ◽  
Tammy Boggiano ◽  
Kalet Leon ◽  
Roberto Mulet
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Media Optimization ◽  
Continuous Cultures ◽  
Genome Scale

scholarly journals Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Parizad Babaei ◽  
Tahereh Ghasemi-Kahrizsangi ◽  
Sayed-Amir Marashi
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Closely Related Species ◽  
Systematic Analysis ◽  
Reconstruction Process ◽  
Flux Balance ◽  
Wide Range ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

scholarly journals In Silico Analysis of Bioethanol Overproduction by Genetically Modified Microorganisms in Coculture Fermentation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lisha K. Parambil ◽  
Debasis Sarkar
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Genetically Modified ◽  
In Silico Analysis ◽  
Ethanol Productivity ◽  
Microbial Consortia ◽  
Bioethanol Production ◽  
E Coli ◽  
Negative Impacts ◽  
Genome Scale

Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective Escherichia coli strain ZSC113 and glucose-selective wild-type Saccharomyces cerevisiae is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by E. coli strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified E. coli strain ZSC113 and S. cerevisiae that significantly enhance ethanol productivity in batch coculture fermentation.

scholarly journals iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

2016 ◽  
Vol 33 ◽  
pp. 158-168 ◽  
Author(s):  
Stefano Andreozzi ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Kinetic Models ◽  
In Silico ◽  
Metabolic Networks ◽  
Genome Scale ◽  
Reduction Of Uncertainty

scholarly journals Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
You‐Tyun Wang ◽  
Min‐Ru Lin ◽  
Wei‐Chen Chen ◽  
Wu‐Hsiung Wu ◽  
Feng‐Sheng Wang
Keyword(s):  
Metabolic Networks ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancers ◽  
Genome Scale

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

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 F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Abdelhalim Larhlimi ◽  
Laszlo David ◽  
Joachim Selbig ◽  
Alexander Bockmayr
Keyword(s):  
Metabolic Networks ◽  
Flux Coupling ◽  
Genome Scale

From genomes to in silico cells via metabolic networks

2005 ◽  
Vol 16 (3) ◽  
pp. 350-355 ◽  
Author(s):  
Irina Borodina ◽  
Jens Nielsen
Keyword(s):  
In Silico ◽  
Metabolic Networks

scholarly journals Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

2010 ◽  
Vol 4 (1) ◽  
pp. 114 ◽  
Author(s):  
Karin Radrich ◽  
Yoshimasa Tsuruoka ◽  
Paul Dobson ◽  
Albert Gevorgyan ◽  
Neil Swainston ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Genome Scale
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