Energy Demand and Metabolite Partitioning in Spatially Lumped and Distributed Models of Neuron-Astrocyte Complex

TL;DR

This paper uses linear algebra to analyze energy metabolism models of neuron-astrocyte complexes, revealing degrees of freedom, constraints, and metabolic partitioning in both lumped and distributed frameworks.

Contribution

It introduces a systematic approach to identify degrees of freedom and metabolic constraints in spatially distributed neuron-astrocyte energy models.

Findings

Lumped model allows deduction of lactate dehydrogenase direction from glucose partitioning.

Distributed model increases degrees of freedom, affecting metabolic conclusions.

Constraints ensure energetic feasibility and enable estimation of cellular energy needs.

Abstract

The degrees of freedom of multi-compartment mathematical models for energy metabolism of a neuron-astrocyte complex may offer a key to understand the different ways in which the energetic needs of the brain are met. In this paper we address the problem within a steady state framework and we use the techniques of linear algebra to identify the degrees of freedom first in a lumped model, then in its extension to a spatially distributed case. The interpretation of the degrees of freedom in metabolic terms, more specifically in terms of glucose and oxygen partitioning, is then leveraged to derive constraints on the free parameters needed to guarantee that the model is energetically feasible. We also demonstrate how the model can be used to estimate the stoichiometric energy needs of the cells as well as the household energy based on observed oxidative cerebral metabolic rate (CMR) of glucose, and the glutamate cycling. Moreover, our analysis shows that in the lumped model the direction of lactate dehydrogenase (LDH) in the cells can be deduced from the glucose partitioning between the compartments. The extension of the lumped model into a spatially distributed multi-compartment setting that includes diffusion fluxes from capillary to tissue increases the number of degrees of freedom, requiring the use of statistical sampling techniques. The analysis of distributed model reveals that some of the conclusions, e.g., concerning the LDH activity and glucose partitioning, based on a spatially lumped model may no longer hold.