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Journal of Computational Neuroscience

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01-12-2009

An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements

Authors: Mathieu Cloutier, Fiachra B. Bolger, John P. Lowry, Peter Wellstead

Published in: Journal of Computational Neuroscience | Issue 3/2009

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Abstract

An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50 µM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model’s predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.

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Ainscow, E. K., & Brand, M. D. (1999). Top-down Control Analysis of ATP Turnover, Glycolysis and Oxidative Phosphorylation in Rat Hepatocytes. European Journal of Biochemistry, 263, 671–685. doi:10.1046/j.1432-1327.1999.00534.x.CrossRefPubMed

Aubert, A., & Costalat, R. (2005). Interactions between astrocytes and neurons studied using a mathematical of compartimentalized energy metabolism. Journal of Cerebral Blood Flow and Metabolism, 25, 1476–1490. doi:10.1038/sj.jcbfm.9600144.CrossRefPubMed

Aubert, A., Costalat, R., Magistretti, P. J., & Pellerin, L. (2005). Brain lactate kinetics: modeling evidence for neuronal lactate uptake upon activation. Proceedings of the National Academy of Sciences of the United States of America, 102, 16448–16453. doi:10.1073/pnas.0505427102.CrossRefPubMed

Barros, L. F., Bittner, C. X., Loaiza, A., & Porras, O. H. (2007). A Quantitative Overview of Glucose Dynamics in the Gliovascular Unit. Glia, 55, 1222–1237. doi:10.1002/glia.20375.CrossRefPubMed

Bolger, F., Serra, P. A., O’Neill, R. D., Fillenz, M., & Lowry, J. P. (2006). Real-time monitoring of brain extracellular lactate. In G. Di Chiara, E. Carboni, V. Valentini, E. Acquas, V. Bassareo & C. Cadoni (Eds.), Monitoring Molecules in Neuroscience, pp. p286–288. Cagliara, Italy: University of Cagliari Press.

Brown, A. M., Sickmann, H. M., Fosgerau, K., Lund, T. M., Schousboe, A., Waagepetersen, H. S., et al. (2005). Astrocytes glycogen metabolism is required for neuronal activity during aglycemia or intense stimulation in mouse white matter. Journal of Neuroscience Research, 79, 74–80. doi:10.1002/jnr.20335.CrossRefPubMed

Brown, A. M., & Ransom, B. R. (2007). Astrocytes Glycogen and Brain Energy Metabolism. Glia, 55, 1263–1271. doi:10.1002/glia.20557.CrossRefPubMed

Cakir, T., Aslan, S., Saybasili, H., Akin, A., & Ulgen, K. (2007). Reconstruction and Flux Analysis of Coupling Between Metabolic Pathways of Astrocytes and Neurons: Application to Cerebral Hypoxia. Theoretical Biology & Medical Modelling, 4, 48–66. doi:10.1186/1742-4682-4-48.CrossRef

Fillenz, M., & Lowry, J. P. (1998). Studies of the Source of Glucose in the Extracellular Compartment of the Rat Brain. Developmental Neuroscience, 20, 365–368. doi:10.1159/000017332.CrossRefPubMed

Forsyth, R. J. (1996). Astrocytes and the delivery of glucose from plasma to neurons. Neurochemistry International, 28(3), 231–241. doi:10.1016/0197-0186(95)00094-1.CrossRefPubMed

Gjedde, A. (1997). The Relation Between Brain Function and Cerebral Blood Flow and Metabolism. In H. H. Batjer (Ed.), Cerebrovascular disease, pp. 23–40. USA, Lippinscott-Raven: Philadelphia.

Gjedde, A. (2002). Coupling of Blood Flow to Neuronal Excitability. In W. Walz (Ed.), The Neuronal Environment: Brain Homeostasis in Health and Disease, pp. 432–444. NJ, USA, Humana Press: Totowa.

Gruetter, R., Seaquist, E. R., & Ugurbil, K. (2001). A mathematical model of compartmentalized neurotransmitter metabolism in the human brain. American Journal of Physiology. Endocrinology and Metabolism, 281, 100–112.

Haefner, J.W. (1996) Modelling Biological Systems: Principles and Applications. New York, ITP Chapman & Hall.

Heinrich, R., & Schuster, S.(1996) The regulation of cellular systems. New York, ITP Chapman & Hall.

Hyder, F., Patel, A. B., Gjedde, A., Rothman, D. L., Behar, K. L., & Shulman, R. G. (2006). Neuronal-Glial Glucose Oxidation and Glutamatergic-GABAergic Function. Journal of Cerebral Blood Flow and Metabolism, 26, 865–877. doi:10.1038/sj.jcbfm.9600263.CrossRefPubMed

Lowry, J. P., & O’Neill, R. D. (2006) Neuroanalytical chemistry in vivo using electrochemical sensors in Encyclopedia of Sensors, Grimes, C.A., Dickey, E.C. & Pishko, M.V ed(s)., American Scientific Publishers, California, USA.

Magistretti P. J. (2006) Neuron-glia metabolic coupling and plasticity. The Journal of Experimental Biology. 209, 2304–2311.

McNay, E. C., & Gold, P. E. (1999). Extracellular glucose concentrations in the rat hippocampus measured by zero-net-flux: effects of microdialysis flow rate, strain, and age. Journal of Neurochemistry, 72, 785–790. doi:10.1046/j.1471-4159.1999.720785.x.CrossRefPubMed

McNay, E. C., McCarty, R. C., & Gold, P. E. (2001). Fluctuations in Brain Glucose Concentration during Behavioral Testing: Dissociations between Brain Areas and between Brain and Blood. Neurobiology of Learning and Memory, 75(3), 325–337. doi:10.1006/nlme.2000.3976.CrossRefPubMed

Miller, G. (2008). Growing pains for fMRI. Science, 320, 1412–1414. doi:10.1126/science.320.5882.1412.CrossRefPubMed

Pellerin, L., & Magistretti, P. J. (1994). Glutamate uptake into astrocytes stimulates aerobic glycolysis: A mechanism coupling neuronal activity to glucose utilization. Proceedings of the National Academy of Sciences of the United States of America, 91, 10625–10629. doi:10.1073/pnas.91.22.10625.CrossRefPubMed

Pellerin, L., Bouzier-Sore, A. K., Auber, A., Serres, S., Merle, M., Costalat, R., et al. (2007). Activity-Dependant Regulation of Energy Metabolism by Astrocytes: An Update. Glia, 55, 1251–1262. doi:10.1002/glia.20528.CrossRefPubMed

Schmidt, H., & Jirstand, M. (2006). Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology. Bioinformatics (Oxford, England), 22, 514–515. doi:10.1093/bioinformatics/bti799

Shen, J., Petersen, K., Behar, K., Brown, P., Nixon, T., Mason, G., et al. (1999). Determination of the rate of the glutamate/glutamine cycle in the human brain by in vivo 13C NMR. Proceedings of the National Academy of Sciences of the United States of America, 96, 8235–8240. doi:10.1073/pnas.96.14.8235.CrossRefPubMed

Shestov, A. A., Valette, J., Ugurbil, K., & Henry, P.-G. (2007). On the reliability of ¹³C metabolic modeling with two-compartment neuronal-glial models. Journal of Neuroscience Research, 85(15), 3294–3303. doi:10.1002/jnr.21269.CrossRefPubMed

Simpson, I. A., Carruthers, A., & Vannucci, S. J. (2007). Supply and Demand in Cerebral Energy Metabolism: The Role of Nutrient Transporters. Journal of Cerebral Blood Flow and Metabolism, 27, 1766–1791. doi:10.1038/sj.jcbfm.9600521.CrossRefPubMed

Uffmann, K., & Gruetter, R. (2007). Mathematical modeling of ¹³C label incorporation of the TCA cycle: The concept of composite precursor function. Journal of Neuroscience Research, 85(15), 3304–3317. doi:10.1002/jnr.21392.CrossRefPubMed

Underwood, A. H., & Newsholme, E. A. (1965). Properties of Phosphofructokinase From Rat Liver and Their Relation to the Control of Glycolysis and Gluconeogenesis. The Biochemical Journal, 95, 868–875.PubMed

Varma, A., & Palsson, B. O. (1994). Metabolic Flux Balancing: Basic concepts, Scientific and Practical Use. Bio/Technology, 12, 994–998. doi:10.1038/nbt1094-994.CrossRef

Zwingmann, C., & Butterworth, R. (2005). An update on the role of brain glutamine synthesis and its relation to cell-specific energy metabolism in the hyperammonemic brain: Further studies using NMR spectroscopy. Neurochemistry International, 47, 19–30. doi:10.1016/j.neuint.2005.04.003.CrossRefPubMed

Title: An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements
Authors: Mathieu Cloutier
Fiachra B. Bolger
John P. Lowry
Peter Wellstead
Publication date: 01-12-2009
Publisher: Springer US
Published in: Journal of Computational Neuroscience / Issue 3/2009
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-009-0152-8

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