{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1803200001?filename=modelYR2017.xml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Rozendaal YJW"],"curationStatus":["Non-curated"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1803200001"],"publication_pubmed":["29879115"],"isPrivate":["false"],"repository":["BioModels"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Rozendaal2018   Model Integrating Glucose and Lipid Dynamics"],"publication_year":["2018"],"submissionId":["MODEL1803200001"],"publication_authors":["Rozendaal YJW, Yanan Wang, Yared Paalvast, Lauren L Tambyrajah, Zhuang Li, Ko Willems van Dijk, Patrick C N Rensen, Jan A Kuivenhoven, Albert K Groen, Peter A J Hilbers, Natal van Riel"],"first_author":["Rozendaal YJW"],"publication":["29879115,\n                            The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.. 6, 14.\n                            Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands."],"submitter_mail":["y.j.w.rozendaal@tue.nl"],"submitter_affiliation":["Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands."],"pubmed_abstract":["The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available."],"pubmed_title":["In vivo and in silico dynamics of the development of Metabolic Syndrome."],"pubmed_authors":["Rozendaal Yvonne J W YJW, Wang Yanan Y, Paalvast Yared Y, Tambyrajah Lauren L LL, Li Zhuang Z, Willems van Dijk Ko K, Rensen Patrick C N PCN, Kuivenhoven Jan A JA, Groen Albert K AK, Hilbers Peter A J PAJ, van Riel Natal A W NAW"],"name_synonyms":["gluco-hexose, glucose, D-Glucose, lipids, (beta-D)-Isomer, D Glucose, Glucose, Lipid., Glukose, Dextrose, Monohydrate, (alpha-D)-Isomer, (DL)-Isomer, Glucose Monohydrate, Anhydrous, DL-glucose, Anhydrous Dextrose, Glc"],"pubmed_abstract_synonyms":["APO-E, Apo E Isoproteins, Food Patterns, BODYFAT, CT11259, single-organism developmental process, cert, determination, Laboratory, acetylglucosaminyltransferase-like 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Insulin resistance syndrome, Cardiovascular Syndrome, Metabolic syndrome X, Long-Term Effects, PE-1, BPIFF, Insulin Sensitivity, Liver steatosis, screening, l(2)gd-1, BcDNA:LD22582, gyltl1b-b, frequency, CASP14, mouse, Longterm Effect, Metabolic Syndromes, Cardiovascular Syndromes, Procedure, CD36, alpha-lecithin cholesterol acyltransferase deficiency, predicted, AI255918, LDLCQ5, Dyslipoproteinemias, Body Fat, Glucose Tolerance, MDDGA6, mKIAA0609, Lipid, Mini-ICE, Obesity [Ambiguous], Obese (finding), Diseases, OBESITY, (alpha-D)-Isomer, KIAA0609, Glucose Tolerances, acetylglucosaminyltransferase-like 1A, Pe1, l(2)k07918, fg, D-Glucose, Mus musculus, HDLCQ10, gyltl1b, gpbp, Caspase-14 subunit p10, maleate, mice, mdc1d, Swiss Mouse, CHDS7, Control, l(2)gd-l, signs, lipid transfer protein I, Adiposity, Caspase-14 subunit p19, Methodological, Impaired, Controls, MICE, LARGE_HUMAN, Methodological Study, surveillance, morbidity, PE1, Abnormal glucose tolerance, domesticus, Dysmetabolic Syndrome X, Phenotypes, disease, MDC1D, l(2)fd, Syndromes, enr, Dietary Patterns, Tolerance, Patient, bA110J1.4, D Glucose, Faf, Ft, Steatosis, Metabolic Cardiovascular, Thrombospondin receptor, medical condition., Mouse, Fat, FAT, Syndrome X, Glucose Monohydrate, Dyslipidaemia, Regulation, gluco-hexose, Regulations, lipids, 3.4.22.-, other disease, SCARB3, LCATA deficiency, Dyslipidemia, Procedures, Effects, Glucose, obesity disease, GPIIIB, Gene, mini-ICE, Metabolic Syndrome, stard11, fat, hFat1, BDPLT10, Dm Fat, LARGE1, Patterns, Dietary Cholesterol, froggy, Gyltl1a, jecur, Obesity, alpha-lecithin:cholesterol acyltransferase deficiency, Dysmetabolic, method, Cardiometabolic Syndromes, APOEA, CDHF7, IR, House, Cholest-5-en-3-ol (3beta)-, method used in an experiment, Insulin, PAS-4, Studies, Diets, disease or disorder, Mus musculus domesticus, Scarb3, CG3352, GP3B, Mice, Technique, Partial LCAT deficiency, dyslipoproteinemic corneal dystrophy, study, LPG, Food Pattern, Metabolic Syndrome X, Longterm, occurrence, Swiss, MDDGB6, Having too much body fat, prevalence, Fatty liver, 79/18, LARGE, Overweight and obesity, Long-Term, CG1945, Cardiometabolic Syndrome, Expressions, non-neoplastic, Study, Pattern, BPFD#36, FED, certl, Clients, Syndrome, Glycoprotein IIIb, bodyfat, disorder, Sensitivity, Long-Term Effect, Expression, Dietary Pattern, CPH, Anhydrous, incidence, Apoproteins E, Morbidities, DmelCG3352, Glucose Intolerances, findings, (beta-D)-Isomer, Tolerances, Metabolic X, R74677, Formal Social Control, Males, disorders, BcDNA.LD22582, l(2)gd2, medical condition, Client, ME5, metabolic syndrome X, AI414410, obesity, development, Impaired Glucose Tolerance, Drug resistance to insulin (disorder), Platelet glycoprotein IV, PASIV, Mus, Social Control, Long Term Effects, Systems, chemical analysis, fatty acid metabolism disorder, Resistance, condition, PAS IV, Cholesterin, Cph-1, outbreaks, obesity disorder, CDHR8, Obesity (disorder), Platelet collagen receptor, Dyslipoproteinemia, Leukocyte differentiation antigen CD36, distinct, Metabolic, Fatty infiltration of liver, alpha-LCAT deficiency, Intolerances, Dietary, Apoprotein (E), Obesity NOS, postnatal growth, Apolipoprotein E, House Mice, Age symptoms begin, Reaven, Cardiometabolic, endemics, Longterm Effects, Anhydrous Dextrose, Laboratory Mice, plan specification, male human body, Livers, Insulin Resistance Syndrome X, cholesterol metabolism, Insulin Resistance, like-glycosyltransferase, fatty depot, adipose, DmelCG1945, Apo E, Impaired Glucose, l(2)fat, l(2)24Da, epidemics, regulation, assay, GPIV, Reaven Syndrome X, growth, Laboratory Mouse, l(2)79/18, glycosyltransferase-like protein LARGE1, Glc"],"description_synonyms":["Gpi, Org, ORG, mOC-X, AI461847, Gpi-1r, Nlk, Gpi-1s, Phi, Gpi-1t, Pgi, Gpi1-r, Gpi1-s, NK|GPI, Gpi-1, MF, NK/GPI., Gpi1-t, Amf, Bglap-rs1, NK, Gpi1s"],"pubmed_title_synonyms":["Metabolic X., single-organism developmental process, Metabolic Syndrome X, Metabolic, postnatal development, postnatal growth, Metabolic Syndromes, X Syndrome, growth and development, Metabolic Syndrome, Cardiovascular Syndromes, Cardiometabolic Syndrome, Reaven, Cardiometabolic, metabolic syndrome X, Metabolic X Syndrome, Dysmetabolic Syndrome X, development, Insulin Resistance Syndrome X, Dysmetabolic, Cardiometabolic Syndromes, Insulin Resistance, Syndromes, Metabolic Cardiovascular Syndrome, Syndrome, Metabolic Cardiovascular, MetS, Syndrome X, Reaven Syndrome X, Insulin resistance syndrome, growth, Cardiovascular Syndrome, Metabolic syndrome X"],"additional_accession":[]},"is_claimable":false,"name":"Rozendaal2018 - Model Integrating Glucose and Lipid Dynamics","description":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006145","dates":{"last_modification":"2019-02-07","publication":"2019-02-07","submission":"2018-03-20"},"accession":"MODEL1803200001","cross_references":{"pubmed":["29879115"],"biomodels__db":["MODEL1803200001"]}}