Mechanistic Models of Astrocytic Glucose Metabolism Calibrated on PET Images

  • Paola Lecca
  • Michela Lecca
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 9)


We presents two models of glucose metabolism in astrocytes based on ordinary differential equations calibrated on \({}^{18}\)F-deoxyglucose PET images. The signals detected during physiological activation of the brain with \({}^{18}\)F-deoxyglucose PET reflect predominantly uptake of this tracer into astrocytes. This notion provides a cellular and molecular basis for the FDG PET technique. In recent years the functional brain imaging has experienced enormous advances. These advancements provided new observational data about the inter- and intra-cellular mechanisms of the brain glucose metabolism. Our models specify of the molecular interactions governing the energy metabolism. The first model describes the glutamate-stimulated glucose uptake and use into astrocytes. It consists of a set of ordinary differential equations, each of which specifying the time-behavior of the main molecular species involved in the astrocytic glucose use (i.e. glutamate, glucose, \(\mathrm{{Na}}^{+}\), \(\beta \)-threohydroxyaspartate) and the dynamical rates of glutamate, glucose and \(\mathrm{{ Na}}^{+}\) uptake. The second model includes also the effects of inter-cellular waves of \(\mathrm{{Na}}^{+}\) and \(\mathrm{{Ca}}^{2+}\) generated by astrocytes on the glucose metabolism. The kinetic rates constants of the models have been identified by fitting the sets of ordinary differential equations to dynamic Positron Emision Tomography scans of 31 patients.


Positron Emission Tomography Glucose Uptake Positron Emission Tomography Image Glutamate Release Glutamate Transporter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author thanks Claus Svarer of Neurobiology Research Unit Rigshospitalet of Copenhagen for having provided the database the dynamic PET images used in this work and for his precious suggestions.


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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.The Microsoft Research—University of Trento Centre for Computational and Systems BiologyRoveretoItaly
  2. 2.Laboratory of Computational OncologyCentre for Integrative Biology, University of Trento Povo/TrentoItaly
  3. 3.Fondazione Bruno Kessler—Center for Information Technology, Research Unit Technologies of Vision TrentoItaly

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