Metabolic Modeling of Dynamic 13C NMR Isotopomer Data in the Brain In Vivo: Fast Screening of Metabolic Models Using Automated Generation of Differential Equations

Brice Tiret, Alexander A. Shestov, Julien Valette, Pierre Gilles Henry

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Most current brain metabolic models are not capable of taking into account the dynamic isotopomer information available from fine structure multiplets in 13C spectra, due to the difficulty of implementing such models. Here we present a new approach that allows automatic implementation of multi-compartment metabolic models capable of fitting any number of 13C isotopomer curves in the brain. The new automated approach also makes it possible to quickly modify and test new models to best describe the experimental data. We demonstrate the power of the new approach by testing the effect of adding separate pyruvate pools in astrocytes and neurons, and adding a vesicular neuronal glutamate pool. Including both changes reduced the global fit residual by half and pointed to dilution of label prior to entry into the astrocytic TCA cycle as the main source of glutamine dilution. The glutamate–glutamine cycle rate was particularly sensitive to changes in the model.

Original languageEnglish (US)
Pages (from-to)2482-2492
Number of pages11
JournalNeurochemical Research
Volume40
Issue number12
DOIs
StatePublished - Dec 1 2015

Keywords

  • C
  • Metabolic modeling
  • Neuronal-glial metabolism
  • Two-compartment

Fingerprint Dive into the research topics of 'Metabolic Modeling of Dynamic <sup>13</sup>C NMR Isotopomer Data in the Brain In Vivo: Fast Screening of Metabolic Models Using Automated Generation of Differential Equations'. Together they form a unique fingerprint.

Cite this