Use of graph theory for biomimetic microchannel network blood flow analysis

Abstract

This paper presents a novel application of graph theory to represent and analyze microcirculatory networks, specifical-ly a biomimetic microfluidic chip designed to mimic the human retina. Utilizing experimental data obtained via high-speed video microscopy and Particle Image Veloci-metry (PIV), the study develops a comprehensive graph-based framework. Key contributions include mapping exper-imental images onto a labeled retinal network, encoding fluid mechanical properties in graph nodes and edges, and storing experimental data in an organized structure. Results demonstrate the feasibility of the approach with a fully la-beled network of 193 channels, offering insights into fluid dynamics and mass conservation in microcirculatory sys-tems. The methodology facilitates future microfluidics re-search by providing a robust and scalable data storage and analysis tool, though further automation is needed to opti-mize workflow.