{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1507180033?filename=MODEL1507180033.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Nicolas Le Novère"],"curationStatus":["Non-curated"],"modellingApproach":["constraint-based model"],"levelVersion":["L3V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1507180033"],"publication_pubmed":["19321003"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Mo2009   Genome scale metabolic network of Saccharomyces cerevisiae (iMM904)"],"publication_year":["2009"],"submissionId":["MODEL1507180033"],"modelFlag":["Non Kinetic"],"publication_authors":["Monica L Mo, Bernhard O Palsson, Markus J Herrgård"],"first_author":["Monica L Mo"],"publication":["19321003,\n                            <h4>Background</h4>Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.<h4>Results</h4>We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The iMM904 metabolic network was reconstructed based on an existing genome-scale network, iND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the iMM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed.<h4>Conclusion</h4>Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states.. null, 3.\n                            Department of Bioengineering, University of California-San Diego, La Jolla, CA 92093, USA."],"submitter_mail":["n.lenovere@gmail.com"],"submitter_affiliation":["The Babraham Institute"],"pubmed_abstract":["<h4>Background</h4>Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.<h4>Results</h4>We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The iMM904 metabolic network was reconstructed based on an existing genome-scale network, iND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the iMM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed.<h4>Conclusion</h4>Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states."],"pubmed_title":["Connecting extracellular metabolomic measurements to intracellular flux states in yeast."],"pubmed_authors":["Mo Monica L ML, Palsson Bernhard O BO, Herrgård Markus J MJ"],"name_synonyms":["Saccharomyces oviformis, Yeast, scale tissue, lager beer yeast., scale, Genomes, Saccharomyces cerevisiae 'var. diastaticus', plant peltate hair, peltate hair, Brewer's, baker's yeast, Saccharomyes cerevisiae, Baker, whole genome, Saccharomyces uvarum var. melibiosus, Saccharomyces italicus, Saccaromyces cerevisiae, S. cerevisiae, Baker's Yeasts, Sccharomyces cerevisiae, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, S cerevisiae, Candida robusta, Saccharomyces diastaticus, Saccharomyces capensis, yeast, Baker's, Mycoderma cerevisiae, 1883, scales, Baker's Yeast, brewer's yeast, Brewer's Yeast, Baker Yeast"],"pubmed_abstract_synonyms":["biochemical pathways, Forms, Ammonium, other disease, scale tissue, nucleocytoplasm, Materials, human being, exocrine gland fluid, Procedures, exocrine gland fluid or secretion, P62, peltate hair, postnatal development, number, Metabonomic, Brewer's, baker's yeast, Gene, Metabonomics, growth and development, Profiles, sci, presence, Saccharomyces italicus, [NH4](+), Gene Deletions, metabolite levels, Human, ammonium cation, Techniques, sampling, diseases, Homo sapiens, Method, azanium, yeast, Studies, disease or disorder, HOW, How, diseases and disorders, Metabolomic, Metabolic Profile, Sprains, Technique, Man, l(3)j5D5, lager beer yeast, Metabolic Profiles, protoplasm, 24B, study, human disease, l(3)s2612, Genetic, Man (Taxonomy), protoplast, Genomes, Metabolomes, catabolism, Profile, plant peltate hair, ammonium ion, stru, Saccharomyes cerevisiae, Bodily, Baker, l(3)S053606, Deletions, procedures, Deletion, Saccharomyces uvarum var. melibiosus, CG10293, ecotype, S. cerevisiae, non-neoplastic, genetic, Study, l(3)j5B5, secreted substance, S cerevisiae, Secretion, Candida robusta, Methodological Studies, bodily secretion, Saccharomyces capensis, Secretions, DmelCG10293, Baker's, disorder, biotransformation, Homo sapiens disease, Mycoderma cerevisiae, 1883, Strains, brewer's yeast, constitutitional genetic, Ammonium(1+), 0904/17, degradation, Saccharomyces cerevisiae 'var. diastaticus', clone 2.39, external secretion, Modern, Sprain, disorders, familial, NH4+, medical condition, qkr, Procedure, l(3)S090417, Cistrons, results, Saccaromyces cerevisiae, predicted, strain, Baker's Yeasts, Sccharomyces cerevisiae, development, exocrine gland fluid/secretion, Bodily Secretion, metabolite traits, count in organism, NH4(+), Saccharomyces diastaticus, SZ1, KH93F, Diseases, Strain, condition, Genetic Materials, secretion, cultivar, background, techniques, internal to cell, scales, Genetic Material, who, Saccharomyces oviformis, Yeast, ammonium, scale, biodegradation, Metabolic, growth pattern, non-developmental growth, postnatal growth, whole genome, Who/How, Methodological, Methodological Study, human, introduction, extracellular, Phenotypes, disease, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, sample collection, Material, Modern Man, qkr[93F], anon-EST:Liang-2.39, Cistron, medical condition., inherited genetic, Baker's Yeast, Strains and Sprains, growth, hereditary, exocrine gland secretion, Brewer's Yeast, Baker Yeast, methodology"],"description_synonyms":["Desc, DESCR., Description, Descriptive, Descriptor, description, Product Description/Appearance"],"pubmed_title_synonyms":["protoplasm, Saccharomyces oviformis, extracellular, Sccharomyces cerevisiae, Yeast, nucleocytoplasm, Candida robusta, lager beer yeast., protoplast, Saccharomyces capensis, yeast, Saccharomyes cerevisiae, baker's yeast, internal to cell, brewer's yeast, Saccharomyces uvarum var. melibiosus, Saccharomyces italicus, Saccaromyces cerevisiae"],"additional_accession":[]},"is_claimable":false,"name":"Mo2009 - Genome-scale metabolic network of Saccharomyces cerevisiae (iMM904)","description":"No description","dates":{"last_modification":"2015-07-28","publication":"2015-07-30","submission":"2015-07-18"},"accession":"MODEL1507180033","cross_references":{"pubmed":["19321003"],"biomodels__db":["MODEL1507180033"]}}