Metabolic network modeling with model organisms

Yılmaz, L. Şafak; Walhout, Albertha J.M.

doi:10.1016/j.cbpa.2016.12.025

Cited by 52 publications

(40 citation statements)

References 61 publications

(71 reference 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“…Additionally, various public but unpublished metabolic reconstructions exist for C. elegans such as SolCyc (http://solcyc.solgenomics.net) and BMID000000141468 (Buchel et al 2013).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%

Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain ofC. elegans

Chan

Hattwell

Ebert

et al. 2017

Preprint

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Section: Discussionmentioning

confidence: 99%

Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain ofC. elegans

Chan

Hattwell

Ebert

et al. 2017

Preprint

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“…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%

Genome-Scale Metabolic Reconstruction of Acetobacter pasteurianus 386B, a Candidate Functional Starter Culture for Cocoa Bean Fermentation

et al. 2019

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Acetobacter pasteurianus 386B is a candidate functional starter culture for the cocoa bean fermentation process. To allow in silico simulations of its related metabolism in response to different environmental conditions, a genome-scale metabolic model for A. pasteurianus 386B was reconstructed. This is the first genome-scale metabolic model reconstruction for a member of the genus Acetobacter. The metabolic network reconstruction process was based on extensive genome re-annotation and comparative genomics analyses. The information content related to the functional annotation of metabolic enzymes and transporters was placed in a metabolic context by exploring and curating a Pathway/Genome Database of A. pasteurianus 386B using the Pathway Tools software. Metabolic reactions and curated gene-protein-reaction associations were bundled into a genome-scale metabolic model of A. pasteurianus 386B, named iAp386B454, containing 454 genes, 322 reactions, and 296 metabolites embedded in two cellular compartments. The reconstructed model was validated by performing growth experiments in a defined medium, which revealed that lactic acid as the sole carbon source could sustain growth of this strain. Further, the reconstruction of the A. pasteurianus 386B genome-scale metabolic model revealed knowledge gaps concerning the metabolism of this strain, especially related to the biosynthesis of its cell envelope and the presence or absence of metabolite transporters.

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“…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%

Effects of MCHM on yeast metabolism

Pupo

Gallagher

2019

Preprint

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On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM's toxicological data were limited, an issue that have been addressed by different studies focused on understanding the immediate and longterm effects of MCHM on human health and the environment. Using S. cerevisiae as a model organism we study the effect of acute exposition to crude MCHM on metabolism. Yeasts were treated with MCHM 3.9 mM in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. 181 and 66 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 38 and 34 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 72 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis and decrease the levels of ergosterol, with a potential impact in the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cell's growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.

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Metabolic network modeling with model organisms

Cited by 52 publications

References 61 publications

Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain ofC. elegans

Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain ofC. elegans

Genome-Scale Metabolic Reconstruction of Acetobacter pasteurianus 386B, a Candidate Functional Starter Culture for Cocoa Bean Fermentation

Effects of MCHM on yeast metabolism

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