2017
DOI: 10.1016/j.cbpa.2016.12.025
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Metabolic network modeling with model organisms

Abstract: Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basi… Show more

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Cited by 53 publications
(40 citation statements)
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“…Additionally, various public but unpublished metabolic reconstructions exist for C. elegans such as SolCyc (http://solcyc.solgenomics.net) and BMID000000141468 (Buchel et al 2013). The development of multiple models for a single organism is common, an example being Saccharomyces cerevisiae, with over two dozen metabolic models (Yilmaz & Walhout 2017). As models of the same organism are generated to study a particular characteristic or application, they complement each other, thus providing a more complete overview of the information available (van Heck et al 2016).…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, various public but unpublished metabolic reconstructions exist for C. elegans such as SolCyc (http://solcyc.solgenomics.net) and BMID000000141468 (Buchel et al 2013). The development of multiple models for a single organism is common, an example being Saccharomyces cerevisiae, with over two dozen metabolic models (Yilmaz & Walhout 2017). As models of the same organism are generated to study a particular characteristic or application, they complement each other, thus providing a more complete overview of the information available (van Heck et al 2016).…”
Section: Discussionmentioning
confidence: 99%
“…So far, genome-scale metabolic models (GEMs) have been reconstructed for many organisms, ranging from bacteria and archaea to fungi, plants, and even human cell lines (Ruppin et al, 2010;Yilmaz and Walhout, 2017). For AAB, the only GEMs that are currently available are those for Gluconobacter oxydans 621H, an industrially important bacterium due to its property of oxidizing a wide range of carbohydrates, and for Komagataeibacter nataicola RZS01, a bacterial cellulose producer (Wu et al, 2014;Zhang et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Focused on the analysis of the whole repertoires of endogenous or exogenous metabolites that are present in a biological system at a given time point metabolomics serves as a link between genotype and phenotype (Aliferis and Chrysayi-Tokousbalides, 2011;Ibáñez et al, 2013). Metabolomics is an extremely useful tool in the analysis of the metabolic modifications induced by potentially toxic The accuracy of FBA predictions can be improved by the integration of experimental data (Yilmaz and Walhout, 2017) . Several methods have been developed to this end, allowing the integration of transcriptomics data: such as E-Flux (Colijn et al, 2009) , omFBA (Guo and Feng, 2016) and transcriptional regulated flux balance analysis (TRFBA) (Motamedian et al, 2017) ; proteomics data: GECKO (a method that enhances a genome-scale metabolic models to account for enzymes as part of reactions) (Sánchez et al, 2017) ; and metabolomics data: unsteady-state flux balance analysis (uFBA) (Bordbar et al, 2017) .…”
Section: Introductionmentioning
confidence: 99%