Oliveira2022 - GSM Model of the Xylella fastidiosa’s_iMS508
Ontology highlight
ABSTRACT:
SUBMITTER: 
Alexandre Oliveira
PROVIDER: MODEL2205020002 | BioModels | 2023-03-10
REPOSITORIES: BioModels
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MODEL2205020002?filename=iMS508.xml | Xml | |||
| MODEL2205020002?filename=iMS508_FROG_report.zip | Other | |||
| MODEL2205020002?filename=miniFROG%20Report_Oliveira2022.xlsx | Xlsx |
Items per page: 5 1 - 3 of 3 |
Similar Datasets
Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
2019-10-22 | MSV000084488 | MassIVE
Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions.
2012-08-03 | GSE39785 | GEO
Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.
2012-08-02 | E-GEOD-39785 | biostudies-arrayexpress
Project description:Myceliophthora thermophila is a thermophilic fungus with great biotechnological characteristics for industrial applications, which can degrade and utilize all major polysaccharides in plant biomass. Nowadays, it has been developing into a platform for production of enzyme, commodity chemicals and biofuels. Therefore, an accurate genome-scale metabolic model would be an accelerator for this fungus becoming a universal chassis for biomanufacturing. Here we present a genome-scale metabolic model for M. thermophila constructed using an auto-generating pipeline with consequent thorough manual curation. Temperature plays a basic and critical role for the microbe growth. we are particularly interested in the genome wide response at metabolic layer of M. thermophilia as it is a thermophlic fungus. To study the effects of temperature on metabolic characteristics of M. thermophila growth, the fungus was cultivated under different temperature. The metabolic rearrangement predicted using context-specific GEMs integrating transcriptome data.The developed model provides new insights into thermophilic fungi metabolism and highlights model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus.
2022-09-14 | GSE184074 | GEO
Project description:Flahaut2013 - Genome-scale metabolic model of L.lactis (MG1363)
Genome-scale metabolic model for Lactococcus lactis
MG1363 and its application to the analysis of flavor formation.
This model is described in the article:
Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.
Flahaut NA, Wiersma A, van de Bunt B, Martens DE, Schaap PJ, Sijtsma L, Dos Santos VA, de Vos WM
Applied Microbiology and Biotechnology. 2013; 97(19):8729-8739
Abstract:
Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.
This model is hosted on BioModels Database
and identified by: MODEL1310300000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
2005-01-01 | MODEL1310300000 | BioModels
Project description:Reconstruction and analysis genome-scale metabolic model of thermophilic fungus Myceliophthora thermophila
| PRJNA763100 | ENA
Project description:Refsum disease is an inborn error of metabolism that is characterized by a defect in peroxisomal α-oxidation of the branched-chain fatty acid phytanic acid. After clinical suspicion of this disorder, including progressive retinitis pigmentosa and polyneuropathy, Refsum disease is biochemically diagnosed by elevated levels of phytanic acid in plasma and tissues of the patient. To date, no cure exists for Refsum disease, but phytanic acid levels in patients can be reduced by plasmapheresis and a strict diet. In recent years, computational models have become valuable tools to provide insight into the complex behaviour of metabolic networks. Besides the comprehensive models that contain all known metabolic reactions within the human body, several tissue- and cell-type-specific models have been developed. However, while systems biology approaches are widely used for complex diseases, only few studies have been published for inborn errors of metabolism. In this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. We used transcriptomics, exo-metabolomics, and proteomics data, which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanic acid and displays fibroblast-specific metabolic functions. Using this model, we investigated the metabolic phenotype of Refsum disease at the genome scale, and we studied the effect of phytanic acid on cell metabolism. We identified 20 metabolites that were predicted to discriminate between Healthy and Refsum’s Disease patients, whereof several with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.
2020-03-20 | PXD015518 | Pride
Project description:Improving genome-scale metabolic model simulations by measuring exchange fluxes during exponential growth phase
| PRJNA1245327 | ENA
Project description:Predicting Changes in Renal Metabolism after Compound Exposure with a Genome-Scale Metabolic Model
| PRJNA594206 | ENA
Project description:Background Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. Results To investigate the metabolism of N. meningitidis we generated and selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogens biology we constructed a genome-scale metabolic network: GSMN-Nm. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the GSMN-Nm model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium such as a requirement for PEP carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. Conclusions This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight to the pathogens metabolism both in vitro and during infection. Data is also available from <ahref=""http://bugs.sgul.ac.uk/E-BUGS-129"" target=""_blank"">BuG@Sbase</a>
2012-01-23 | E-BUGS-129 | biostudies-arrayexpress