Using graph theory to compute Laplace operators arising in a model for blood flow in capillary network

TL;DR

This paper provides a rigorous mathematical proof related to Laplace operators in models of blood flow in capillary networks, which is crucial for understanding brain oxygenation and related diseases.

Contribution

It offers a rigorous proof of a conjecture involving Laplace operators in capillary network models, extending previous numerical and analytical results.

Findings

Proved the conjecture for a general class of networks.

Connected the number of trees and forests in graphs to blood flow properties.

Enhanced understanding of flow heterogeneities in capillary networks.

Abstract

Maintaining cerebral blood flow is critical for adequate neuronal function. Previous computational models of brain capillary networks have predicted that heterogeneous cerebral capillary flow patterns result in lower brain tissue partial oxygen pressures. It has been suggested that this may lead to number of diseases such as Alzheimer's disease, acute ischemic stroke, traumatic brain injury and ischemic heart disease. We have previously developed a computational model that was used to describe in detail the effect of flow heterogeneities on tissue oxygen levels. The main result in that paper was that, for a general class of capillary networks, perturbations of segment diameters or conductances always lead to decreased oxygen levels. This result was verified using both numerical simulations and mathematical analysis. However, the analysis depended on a novel conjecture concerning the Laplace operator of functions related to the segment flow rates and how they depend on the conductances. The goal of this paper is to give a mathematically rigorous proof of the conjecture for a general class of networks. The proof depends on determining the number of trees and forests in certain graphs arising from the capillary network.