Metabolic modeling of dynamic brain ¹³C NMR multiplet data: concepts and simulations with a two-compartment neuronal-glial model.

Metabolic modeling of dynamic (13)C labeling curves during infusion of (13)C-labeled substrates allows quantitative measurements of metabolic rates in vivo. However metabolic modeling studies performed in the brain to date have only modeled time courses of total isotopic enrichment at individual carbon positions (positional enrichments), not taking advantage of the additional dynamic (13)C isotopomer information available from fine-structure multiplets in (13)C spectra. Here we introduce a new (13)C metabolic modeling approach using the concept of bonded cumulative isotopomers, or bonded cumomers. The direct relationship between bonded cumomers and (13)C multiplets enables fitting of the dynamic multiplet data. The potential of this new approach is demonstrated using Monte-Carlo simulations with a brain two-compartment neuronal-glial model. The precision of positional and cumomer approaches are compared for two different metabolic models (with and without glutamine dilution) and for different infusion protocols ([1,6-(13)C(2)]glucose, [1,2-(13)C(2)]acetate, and double infusion [1,6-(13)C(2)]glucose + [1,2-(13)C(2)]acetate). In all cases, the bonded cumomer approach gives better precision than the positional approach. In addition, of the three different infusion protocols considered here, the double infusion protocol combined with dynamic bonded cumomer modeling appears the most robust for precise determination of all fluxes in the model. The concepts and simulations introduced in the present study set the foundation for taking full advantage of the available dynamic (13)C multiplet data in metabolic modeling.