libRoadRunner 2.0: a high performance SBML simulation and analysis library

Somogyi, E; Bouteiller, Jean-Marie C.; Glazier, James A.; König, Matthias; Medley, Kyle; Swat, Maciej; Sauro, Herbert M.

doi:10.1093/bioinformatics/btac770

Cited by 22 publications

(22 citation statements)

References 37 publications

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“…Finally, we performed model predictions of glycolytic intermediates and insulin response as a function of varying glucose concentrations. The set of differential equations was numerically integrated using basiCO (Bergmann, 2023) based on COPASI (Hoops et al, 2006) and sbmlsim (König, 2021) based on libroadrunner (Welsh et al, 2023;Somogyi et The model consists of glycolysis and insulin secretion coupled to the energy state (ATP/ADP ratio). The GLUT transporter facilitates the uptake of glucose from the plasma into the cell.…”

Section: Kinetic Model and Model Predictionsmentioning

confidence: 99%

“…Finally, we performed model predictions of glycolytic intermediates and insulin response as a function of varying glucose concentrations. The set of differential equations was numerically integrated using basiCO (Bergmann, 2023) based on COPASI (Hoops et al, 2006) and sbmlsim (König, 2021) based on libroadrunner (Welsh et al, 2023;Somogyi et al, varied as linspace(0.01, 35, num=11) and (Akhtar et al, 1977;Ashcroft et al, 1970Ashcroft et al, , 1973bGiroix et al, 1984;Huang and Joseph, 2014;Liu et al, 1998Liu et al, , 2004Malmgren et al, 2013;Matschinsky and Ellerman, 1968;Miwa et al, 2000;Spégel et al, 2015;Taniguchi et al, 2000;Trus et al, 1979Trus et al, , 1980. For more details, please refer to Sec.…”

Section: Kinetic Model and Model Predictionsmentioning

confidence: 99%

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A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreaticβ-Cell

Raha

König

et al. 2023

Preprint

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The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic beta-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct the first consensus model of GSIS in the pancreatic beta-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and beta-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and equations for insulin secretion coupled to cellular energy state (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the beta-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic beta-cell.

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Section: Kinetic Model and Model Predictionsmentioning

confidence: 99%

“…Finally, we performed model predictions of glycolytic intermediates and insulin response as a function of varying glucose concentrations. The set of differential equations was numerically integrated using basiCO (Bergmann, 2023) based on COPASI (Hoops et al, 2006) and sbmlsim (König, 2021) based on libroadrunner (Welsh et al, 2023;Somogyi et al, varied as linspace(0.01, 35, num=11) and (Akhtar et al, 1977;Ashcroft et al, 1970Ashcroft et al, , 1973bGiroix et al, 1984;Huang and Joseph, 2014;Liu et al, 1998Liu et al, , 2004Malmgren et al, 2013;Matschinsky and Ellerman, 1968;Miwa et al, 2000;Spégel et al, 2015;Taniguchi et al, 2000;Trus et al, 1979Trus et al, , 1980. For more details, please refer to Sec.…”

Section: Kinetic Model and Model Predictionsmentioning

confidence: 99%

A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreaticβ-Cell

Raha

König

et al. 2023

Preprint

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“…This paper used version 0.9.1 of the model (König and Bartsch, 2023). The model was developed using sbmlutils (König, 2022), simulated using sbmlsim (König, 2021) with libroadrunner (Somogyi et al, 2015;Welsh et al, 2023) as the high performance simulator, and visualized using cy3sbml (König et al, 2012).…”

Section: Computational Modelmentioning

confidence: 99%

Simvastatin therapy in different subtypes of hypercholesterolemia – a physiologically based modelling approach

Bartsch

Grzegorzewski

Pujol

et al. 2023

Preprint

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Hypercholesterolemia is a multifaceted plasma lipid disorder with heterogeneous causes including lifestyle and genetic factors. A key feature of hypercholesterolemia is elevated plasma levels of low-density lipoprotein cholesterol (LDL-C). Several genetic variants have been reported to be associated with hypercholesterolemia, known as familial hypercholesterolemia (FH). Important variants affect the LDL receptor (LDLR), which mediates the uptake of LDL-C from the plasma, apoliporotein B (APOB), which is involved in the binding of LDL-C to the LDLR, and proprotein convertase subtilisin/kexin type 9 (PCSK9), which modulates the degradation of the LDLR. A typical treatment for hypercholesterolemia is statin medication, with simvastatin being one of the most commonly prescribed statins. In this work, the LDL-C lowering therapy with simvastatin in hypercholesterolemia was investigated using a computational modeling approach. A physiologically based pharmacokinetic model of simvastatin integrated with a pharmacodynamic model of plasma LDL-C (PBPK/PD) was developed based on extensive data curation. A key component of the model is LDL-C turnover by the liver, consisting of: hepatic cholesterol synthesis with the key enzymes HMG-CoA reductase and HMG-CoA synthase; cholesterol export from the liver as VLDL-C; de novo synthesis of LDLR; transport of LDLR to the membrane; binding of LDL-C by LDLR via APOB; endocytosis of the LDLR-LDL-C complex; recycling of LDLR from the complex. The model was applied to study the effects of simvastatin therapy in hypercholesterolemia due to different causes in the LDLR pathway corresponding to different subtypes of hypercholesterolemia. Model predictions of LDL-C lowering therapy were validated with independent clinical data sets. Key findings are: (i) hepatic LDLR turnover is highly heterogeneous among FH classes; (ii) despite this heterogeneity, simvastatin therapy results in a consistent reduction in plasma LDL-C regardless of class; and (iii) simvastatin therapy shows a dose-dependent reduction in LDL-C. Our model suggests that the underlying cause of hypercholesterolemia does not influence simvastatin therapy. Furthermore, our model supports the treatment strategy of stepwise dose adjustment to achieve target LDL-C levels. Both the model and the database are freely available for reuse.

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“…The model was encoded in the Systems Biology Markup Language (SBML) (Hucka et al, 2019;Keating et al, 2020) and developed using sbmlutils (König, 2021b), a collection of Python utilities for building SBML models, and cy3sbml (König et al, 2012;König and Rodriguez, 2019), a visualization software for SBML. The model is an ordinary differential equation (ODE) model which is numerically solved by sbmlsim (König, 2021a) based on the high-performance SBML simulator libroadrunner (Somogyi et al, 2015;Welsh et al, 2023). The model is available under the CC-BY 4.0 license at https://github.com/matthiaskoenig/chlorzoxazone-model.…”

Section: Modelmentioning

confidence: 99%

A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone

Küttner

Grzegorzewski

Tautenhahn

et al. 2023

Preprint

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The cytochrome P450 (CYP) superfamily of enzymes plays a critical role in the metabolism of drugs, toxins, and endogenous and exogenous compounds. The activity of CYP enzymes can be influenced by a variety of factors, including genetics, diet, age, environmental factors, and disease. Among the major isoforms, CYP2E1 is of particular interest due to its involvement in the metabolism of various low molecular weight chemicals, including alcohols, pharmaceuticals, industrial solvents, and halogenated anesthetics. Metabolic phenotyping of CYPs based on the elimination of test compounds is a useful method for assessing in vivo activity, with chlorzoxazone being the primary probe drug for phenotyping of CYP2E1. The aim of this work was to investigate the effect of changes in CYP2E1 level and activity, ethanol consumption, ethanol abstinence, and liver impairment on the results of metabolic phenotyping with chlorzoxazone. To accomplish this, an extensive pharmacokinetic dataset for chlorzoxazone was established and a physiologically based pharmacokinetic (PBPK) model of chlorzoxazone and its metabolites, 6-hydroxychlorzoxazone and chlorzoxazone-O-glucuronide, was developed and validated. The model incorporates the effect of ethanol consumption on CYP2E1 levels and activity by extending the model with a core ethanol pharmacokinetic model and a CYP2E1 turnover model. The model accurately predicts pharmacokinetic data from several clinical studies and is able to estimate the effect of changes in CYP2E1 levels and activity on chlorzoxazone pharmacokinetics. Regular ethanol consumption induces CYP2E1 over two to three weeks, resulting in increased conversion of chlorzoxazone to 6-hydroxychlorzoxazone and a higher 6-hydroxychlorzoxazone/chlorzoxazone metabolic ratio. After ethanol withdrawal, CYP2E1 levels return to baseline within one week. Importantly, liver impairment has an opposite effect, resulting in reduced liver function via CYP2E1. In alcoholics with liver impairment who also consume ethanol, these factors will have opposite confounding effects on metabolic phenotyping with chlorzoxazone.

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libRoadRunner 2.0: a high performance SBML simulation and analysis library

Cited by 22 publications

References 37 publications

A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreaticβ-Cell

A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreaticβ-Cell

Simvastatin therapy in different subtypes of hypercholesterolemia – a physiologically based modelling approach

A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone

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