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Artículos de revistas sobre el tema "Genome-Scale Metabolic Network (GSMN)"

Autor: Grafiati

Publicado: 25 de enero de 2025

Última modificación: 31 de julio de 2025

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1

Moretti, Sébastien, Van Du T. Tran, Florence Mehl, Mark Ibberson, and Marco Pagni. "MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models." Nucleic Acids Research 49, no. D1 (2020): D570—D574. http://dx.doi.org/10.1093/nar/gkaa992.

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Abstract MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages abou

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2

Nègre, Delphine, Abdelhalim Larhlimi, and Samuel Bertrand. "Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks–What about specialised metabolism?" PLOS ONE 18, no. 8 (2023): e0289757. http://dx.doi.org/10.1371/journal.pone.0289757.

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In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Sca

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3

Nègre, Aite, Belcour, et al. "Genome–Scale Metabolic Networks Shed Light on the Carotenoid Biosynthesis Pathway in the Brown Algae Saccharina japonica and Cladosiphon okamuranus." Antioxidants 8, no. 11 (2019): 564. http://dx.doi.org/10.3390/antiox8110564.

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Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome–Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an

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4

Thananusak, Roypim, Kobkul Laoteng, Nachon Raethong, Yu Zhang, and Wanwipa Vongsangnak. "Metabolic Responses of Carotenoid and Cordycepin Biosynthetic Pathways in Cordyceps militaris under Light-Programming Exposure through Genome-Wide Transcriptional Analysis." Biology 9, no. 9 (2020): 242. http://dx.doi.org/10.3390/biology9090242.

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Cordyceps militaris is currently exploited for commercial production of specialty products as its biomass constituents are enriched in bioactive compounds, such as cordycepin. The rational process development is important for economically feasible production of high quality bioproducts. Light is an abiotic factor affecting the cultivation process of this entomopathogenic fungus, particularly in its carotenoid formation. To uncover the cell response to light exposure, this study aimed to systematically investigate the metabolic responses of C. militaris strain TBRC6039 using integrative genome-

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5

Rodríguez-Mier, Pablo, Nathalie Poupin, Carlo de Blasio, Laurent Le Cam, and Fabien Jourdan. "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks." PLOS Computational Biology 17, no. 2 (2021): e1008730. http://dx.doi.org/10.1371/journal.pcbi.1008730.

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The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the g

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6

Borah, Khushboo, Jacque-Lucca Kearney, Ruma Banerjee, et al. "GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae." PLOS Neglected Tropical Diseases 14, no. 7 (2020): e0007871. http://dx.doi.org/10.1371/journal.pntd.0007871.

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7

Zhang, Lingrui, Bin Wang, Ruiqi Zhang, et al. "Screening of Potential Drug Targets Based on the Genome-Scale Metabolic Network Model of Vibrio parahaemolyticus." Current Issues in Molecular Biology 47, no. 7 (2025): 575. https://doi.org/10.3390/cimb47070575.

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Vibrio parahaemolyticus is a pathogenic bacterium widely distributed in marine environments, posing significant threats to aquatic organisms and human health. The overuse and misuse of antibiotics has led to the development of multidrug- and pan-resistant V. parahaemolyticus strains. There is an urgent need for novel antibacterial therapies with innovative mechanisms of action. In this work, a genome-scale metabolic network model (GMSN) of V. parahaemolyticus, named VPA2061, was reconstructed to predict the metabolites that can be explored as potential drug targets for eliminating V. parahaemo

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8

Liu, Lili, Qian Mei, Zhenning Yu, Tianhao Sun, Zijun Zhang, and Ming Chen. "An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice." Journal of Integrative Bioinformatics 10, no. 2 (2013): 94–102. http://dx.doi.org/10.1515/jib-2013-223.

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Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However,

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9

Gupta, Ankit, Ahmad Ahmad, Dipesh Chothwe, Midhun K. Madhu, Shireesh Srivastava, and Vineet K. Sharma. "Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath." PeerJ 7 (June 14, 2019): e6685. http://dx.doi.org/10.7717/peerj.6685.

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The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph,Methylococcus capsulatusstr. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is namediMC535. The predictive potential of the model was validate

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10

Brunner, James D., Laverne A. Gallegos-Graves, and Marie E. Kroeger. "Inferring microbial interactions with their environment from genomic and metagenomic data." PLOS Computational Biology 19, no. 11 (2023): e1011661. http://dx.doi.org/10.1371/journal.pcbi.1011661.

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Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these tech

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11

Li, Jian, Renliang Sun, Xinjuan Ning, Xinran Wang, and Zhuo Wang. "Genome-Scale Metabolic Model of Actinosynnema pretiosum ATCC 31280 and Its Application for Ansamitocin P-3 Production Improvement." Genes 9, no. 7 (2018): 364. http://dx.doi.org/10.3390/genes9070364.

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Actinosynnema pretiosum ATCC 31280 is the producer of antitumor agent ansamitocin P-3 (AP-3). Understanding of the AP-3 biosynthetic pathway and the whole metabolic network in A. pretiosum is important for the improvement of AP-3 titer. In this study, we reconstructed the first complete Genome-Scale Metabolic Model (GSMM) Aspm1282 for A. pretiosum ATCC 31280 based on the newly sequenced genome, with 87% reactions having definite functional annotation. The model has been validated by effectively predicting growth and the key genes for AP-3 biosynthesis. Then we built condition-specific models f

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12

Zhang, Dandan, Jinyu Chen, Zihui Wang, and Cheng Wang. "Integrated Metabolomic and Network Analysis to Explore the Potential Mechanism of Three Chemical Elicitors in Rapamycin Overproduction." Microorganisms 10, no. 11 (2022): 2205. http://dx.doi.org/10.3390/microorganisms10112205.

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Rapamycin is a polyketide macrocyclic antibiotic with exceptional pharmacological potential. To explore the potential mechanism of rapamycin overproduction, the intracellular metabolic differences of three chemical elicitor treatments were first investigated by combining them with dynamic metabolomics and network analysis. The metabolic response characteristics of each chemical elicitor treatment were identified by a weighted gene co-expression network analysis (WGCNA) model. According to the analysis of the identified metabolic modules, the changes in the cell membrane permeability might play

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13

Belcour, Arnaud, Jeanne Got, Méziane Aite, et al. "Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe." Genome Research 33, no. 6 (2023): 972–87. http://dx.doi.org/10.1101/gr.277056.122.

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Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one a

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14

Witting, Michael. "Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model." Metabolites 10, no. 4 (2020): 130. http://dx.doi.org/10.3390/metabo10040130.

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Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic networks in silico or used for analysis of omics data. Beside correct mass and charge balance, correct structural annotation of metabolites represents an important factor for analysis of these metabolic networks. However, several metabolites in different GSMs have no or only partial structural information associ

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15

Beste, Dany JV, Tracy Hooper, Graham Stewart, et al. "GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism." Genome Biology 8, no. 5 (2007): R89. http://dx.doi.org/10.1186/gb-2007-8-5-r89.

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16

Yu, Hai-Long, Xiao-Long Liang, Zhen-Yang Ge, et al. "Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target." International Journal of Molecular Sciences 25, no. 22 (2024): 12236. http://dx.doi.org/10.3390/ijms252212236.

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Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA

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17

Cooke, Juliette, Maxime Delmas, Cecilia Wieder, et al. "Genome scale metabolic network modelling for metabolic profile predictions." PLOS Computational Biology 20, no. 2 (2024): e1011381. http://dx.doi.org/10.1371/journal.pcbi.1011381.

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Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, compu

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18

Lertampaiporn, Supatcha, Jittisak Senachak, Wassana Taenkaew, Chiraphan Khannapho, and Apiradee Hongsthong. "Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis." Cells 9, no. 9 (2020): 2097. http://dx.doi.org/10.3390/cells9092097.

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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 environmenta

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19

Kim, Byoungjin, Won Jun Kim, Dong In Kim, and Sang Yup Lee. "Applications of genome-scale metabolic network model in metabolic engineering." Journal of Industrial Microbiology & Biotechnology 42, no. 3 (2014): 339–48. http://dx.doi.org/10.1007/s10295-014-1554-9.

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20

Manna, Bharat, and Amit Ghosh. "Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila." Canadian Journal of Biotechnology 1, Special Issue (2017): 134. http://dx.doi.org/10.24870/cjb.2017-a120.

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21

Singh, Dipali, and Martin J. Lercher. "Network reduction methods for genome-scale metabolic models." Cellular and Molecular Life Sciences 77, no. 3 (2019): 481–88. http://dx.doi.org/10.1007/s00018-019-03383-z.

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22

Yilmaz, L. Safak, and Albertha J. M. Walhout. "A Caenorhabditis elegans Genome-Scale Metabolic Network Model." Cell Systems 2, no. 5 (2016): 297–311. http://dx.doi.org/10.1016/j.cels.2016.04.012.

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23

Sarathy, Chaitra, Marian Breuer, Martina Kutmon, Michiel E. Adriaens, Chris T. Evelo, and Ilja C. W. Arts. "Comparison of metabolic states using genome-scale metabolic models." PLOS Computational Biology 17, no. 11 (2021): e1009522. http://dx.doi.org/10.1371/journal.pcbi.1009522.

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Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcas

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24

Sigurdsson, Martin, Neema Jamshidi, Jon J. Jonsson, and Bernhard Ø. Palsson. "Genome-scale network analysis of imprinted human metabolic genes." Epigenetics 4, no. 1 (2009): 43–46. http://dx.doi.org/10.4161/epi.4.1.7603.

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25

Serrano, M. Ángeles, and Francesc Sagués. "Network-based scoring system for genome-scale metabolic reconstructions." BMC Systems Biology 5, no. 1 (2011): 76. http://dx.doi.org/10.1186/1752-0509-5-76.

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26

Burgard, A. P. "Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions." Genome Research 14, no. 2 (2004): 301–12. http://dx.doi.org/10.1101/gr.1926504.

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27

Forster, J. "Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network." Genome Research 13, no. 2 (2003): 244–53. http://dx.doi.org/10.1101/gr.234503.

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28

Han, N. S., Y. J. Kim, and D. Y. Lee. "Genome-scale reconstruction of metabolic network in Leuconostoc mesenteroides." New Biotechnology 25 (September 2009): S358. http://dx.doi.org/10.1016/j.nbt.2009.06.865.

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29

Christian, Nils, Oliver Ebenhöh, and Patrick May. "Improving the genome-scale metabolic network of Arabidopsis thaliana." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 153, no. 2 (2009): S227—S228. http://dx.doi.org/10.1016/j.cbpa.2009.04.570.

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30

Chang, Roger L., Kathleen Andrews, Donghyuk Kim, Zhanwen Li, Adam Godzik, and Bernhard O. Palsson. "Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli." Science 340, no. 6137 (2013): 1220–23. http://dx.doi.org/10.1126/science.1234012.

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Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growt

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31

Fell, David A., Mark G. Poolman, and Albert Gevorgyan. "Building and analysing genome-scale metabolic models." Biochemical Society Transactions 38, no. 5 (2010): 1197–201. http://dx.doi.org/10.1042/bst0381197.

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Reconstructing a model of the metabolic network of an organism from its annotated genome sequence would seem, at first sight, to be one of the most straightforward tasks in functional genomics, even if the various data sources required were never designed with this application in mind. The number of genome-scale metabolic models is, however, lagging far behind the number of sequenced genomes and is likely to continue to do so unless the model-building process can be accelerated. Two aspects that could usefully be improved are the ability to find the sources of error in a nascent model rapidly,

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32

Vargas, C., A. García-Yoldi, J. M. Rodríguez, et al. "Genome-scale reconstruction of the metabolic network in Chromohalobacter salexigens." New Biotechnology 25 (September 2009): S333. http://dx.doi.org/10.1016/j.nbt.2009.06.806.

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33

Portela, Carla, Silas Villas-Bôas, Isabel Rocha, and Eugénio C. Ferreira. "Genome scale metabolic network reconstruction of the pathogen Enterococcus faecalis." IFAC Proceedings Volumes 46, no. 31 (2013): 131–36. http://dx.doi.org/10.3182/20131216-3-in-2044.00067.

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34

Schilling, Christophe H., Markus W. Covert, Iman Famili, George M. Church, Jeremy S. Edwards, and Bernhard O. Palsson. "Genome-Scale Metabolic Model of Helicobacter pylori 26695." Journal of Bacteriology 184, no. 16 (2002): 4582–93. http://dx.doi.org/10.1128/jb.184.16.4582-4593.2002.

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ABSTRACT A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balan

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35

Oberhardt, Matthew A., Jacek Puchałka, Kimberly E. Fryer, Vítor A. P. Martins dos Santos, and Jason A. Papin. "Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1." Journal of Bacteriology 190, no. 8 (2008): 2790–803. http://dx.doi.org/10.1128/jb.01583-07.

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ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aer

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36

Yang, Yi, Xiao-Pan Hu, and Bin-Guang Ma. "Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states." Molecular BioSystems 13, no. 3 (2017): 607–20. http://dx.doi.org/10.1039/c6mb00553e.

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Hosseini, Sayed-Rzgar, Olivier C. Martin, and Andreas Wagner. "Phenotypic innovation through recombination in genome-scale metabolic networks." Proceedings of the Royal Society B: Biological Sciences 283, no. 1839 (2016): 20161536. http://dx.doi.org/10.1098/rspb.2016.1536.

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Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype–phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experim

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38

Richards, Matthew A., Thomas J. Lie, Juan Zhang, Stephen W. Ragsdale, John A. Leigh, and Nathan D. Price. "Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis." Journal of Bacteriology 198, no. 24 (2016): 3379–90. http://dx.doi.org/10.1128/jb.00571-16.

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ABSTRACTHydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus onMethanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather

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39

Schuster, Stefan, Luís F. de Figueiredo, and Christoph Kaleta. "Predicting novel pathways in genome-scale metabolic networks." Biochemical Society Transactions 38, no. 5 (2010): 1202–5. http://dx.doi.org/10.1042/bst0381202.

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Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing

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40

Salehabadi, Ehsan, Ehsan Motamedian, and Seyed Abbas Shojaosadati. "Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers." PLOS ONE 17, no. 3 (2022): e0254270. http://dx.doi.org/10.1371/journal.pone.0254270.

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Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to gen

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41

Babaei, Parizad, Sayed-Amir Marashi, and Sedigheh Asad. "Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501." Molecular BioSystems 11, no. 11 (2015): 3022–32. http://dx.doi.org/10.1039/c5mb00086f.

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42

Kim, Hyun Uk, and Sang Yup Lee. "Applications of genome-scale metabolic network models in the biopharmaceutical industry." Pharmaceutical Bioprocessing 1, no. 4 (2013): 337–39. http://dx.doi.org/10.4155/pbp.13.37.

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43

Imam, Saheed, Safak Yilmaz, Ugur Sohmen, et al. "iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network." BMC Systems Biology 5, no. 1 (2011): 116. http://dx.doi.org/10.1186/1752-0509-5-116.

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44

Wu, Xinsen, Xiaoyang Wang, and Wenyu Lu. "Genome-scale reconstruction of a metabolic network for Gluconobacter oxydans 621H." Biosystems 117 (March 2014): 10–14. http://dx.doi.org/10.1016/j.biosystems.2014.01.001.

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45

Martínez, Verónica S., Lake-Ee Quek, and Lars K. Nielsen. "Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models." Biophysical Journal 107, no. 2 (2014): 493–503. http://dx.doi.org/10.1016/j.bpj.2014.05.029.

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Dunphy, Laura J., and Jason A. Papin. "Biomedical applications of genome-scale metabolic network reconstructions of human pathogens." Current Opinion in Biotechnology 51 (June 2018): 70–79. http://dx.doi.org/10.1016/j.copbio.2017.11.014.

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47

Damiani, Andrew L., Q. Peter He, Thomas W. Jeffries, and Jin Wang. "Comprehensive evaluation of two genome-scale metabolic network models forScheffersomyces stipitis." Biotechnology and Bioengineering 112, no. 6 (2015): 1250–62. http://dx.doi.org/10.1002/bit.25535.

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48

Barona-Gómez, Francisco, Pablo Cruz-Morales, and Lianet Noda-García. "What can genome-scale metabolic network reconstructions do for prokaryotic systematics?" Antonie van Leeuwenhoek 101, no. 1 (2011): 35–43. http://dx.doi.org/10.1007/s10482-011-9655-1.

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49

Li, Gaoyang, Huansheng Cao, and Ying Xu. "Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes." Briefings in Bioinformatics 20, no. 4 (2018): 1590–603. http://dx.doi.org/10.1093/bib/bby022.

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Abstract We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functiona

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50

McCubbin, Tim, R. Axayacatl Gonzalez-Garcia, Robin W. Palfreyman, Chris Stowers, Lars K. Nielsen, and Esteban Marcellin. "A Pan-Genome Guided Metabolic Network Reconstruction of Five Propionibacterium Species Reveals Extensive Metabolic Diversity." Genes 11, no. 10 (2020): 1115. http://dx.doi.org/10.3390/genes11101115.

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Propionibacteria have been studied extensively since the early 1930s due to their relevance to industry and importance as human pathogens. Still, their unique metabolism is far from fully understood. This is partly due to their signature high GC content, which has previously hampered the acquisition of quality sequence data, the accurate annotation of the available genomes, and the functional characterization of genes. The recent completion of the genome sequences for several species has led researchers to reassess the taxonomical classification of the genus Propionibacterium, which has been d

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