Project description:This is the extended model described in eq. 2 of the article: A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell. Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9 Abstract: We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:This is the model described in the article: Interaction of glycolysis and mitochondrial respiration in metabolic oscillations of pancreatic islets. Bertram R, Satin LS, Pedersen MG, Luciani DS, Sherman A. Biophys J. 2007 Mar 1;92(5):1544-55. Pubmed ID: 17172305, doi: 10.1529/biophysj.106.097154. Abstract: Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Bertram, Satin, Pedersen, Luciani, Sherman, 2007 version 02 The original CellML model was created and curated by: Catherine May Lloyd c.lloyd(at)auckland.ac.nz The University of Auckland, Bioengineering Institute This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:The heart uses primarily fatty acids and glucose for deriving energy. The majority of energy in the healthy heart derives from fat utilization, with the remainder coming primarily from the catabolism of glucose. Classical studies by Randle and colleagues describe the ability of the heart to switch its mode of utilization facilely and reversibly between glucose and fatty acids (myocardial glucose-fatty acid cycle or Randle cycle). However, under conditions of pathological stress, reliance of the heart on fatty acids decreases with a concomitant increase in reliance on glucose. It is unclear how such changes in metabolism regulate gene expression in the heart. Therefore, we examined how regulation of glycolysis at the level of phosphofructokinase modulates gene expression in the heart. We performed transcriptomic analysis of hearts from mice expressing either kinase-deficient phosphofructokinase 2 (GlycoLo) or phosphatase-deficient phosphofructokinase 2 (GlycoHi) under the control of the α-MHC promoter, which restricted expression of the transgenes to the heart. Phosphofructokinase 2 only controls the ability of the myocyte to regulate abundance of a single metabolite, F-2,6-P2, which is an allosteric regulator of the rate-limiting and committed step in glycolysis. Parallel radiometric and metabolomic studies showed the expected increases or decreases in glycolytic flux along with diametrically opposite changes in fat metabolism, which is consistent with the myocardial glucose-fatty acid cycle. Transcriptomic analyses showed remarkable changes in gene transcription in these hearts, which indicates that glucose and/or fatty acid metabolism is a driver of transcriptional programs in the heart. Furthermore, glycolytic activity coordinately regulated numerous genes in the heart, including genes important for cardiac remodeling as well as genes regulating gluconeogenic and ancillary biosynthetic pathway activity. These findings reveal that glycolytic rate is a critical regulator of gene expression in the heart and can coordinate programs that modulate cardiac metabolism, growth, and hypertrophy.

Project description:Jiang2007 - GSIS system, Pancreatic Beta Cells Description of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells. This model is described in the article: A kinetic core model of the glucose-stimulated insulin secretion network of pancreatic beta cells. Jiang N, Cox RD, Hancock JM. Mamm Genome 2007 Jul; 18(6-7):508-20. Abstract: The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state. The model reproduces Figure 2 of the paper, and is built using files 'ModelNNT11.xml' and 'changed.m' available from http://www.har.mrc.ac.uk/research/bioinformatics/research_areas/systems_biology.html . A couple of small errors in the model (in the original SBML file 'ModelNNT11.xml') have been corrected. The errors are: v44 now produces Pyr rather than PYR the kinetic law of v27 is now dependent on cytoplasmic (rather than mitochondrial) acetyl CoA and OXA This model is hosted on BioModels Database and identified by: BIOMD0000000239 . 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.

Project description:This a model from the article: Glucose sensing in the pancreatic beta cell: a computational systems analysis. Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556, Abstract: BACKGROUND: Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach. METHODS: We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled. RESULTS: The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential. CONCLUSION: This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells. This model was taken from the Vcell MathModel directory and was converted to SBML This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Westermark2003_Pancreatic_GlycOsc_basic

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A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.

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