The role of vascular complexity on optimal junction exponents

TL;DR

This study investigates how vascular complexity influences the optimal structure of arterial trees, revealing that while overall stability is maintained, key parameters like junction exponents are sensitive to complexity and boundary conditions.

Contribution

It introduces a method to analyze the stability of arterial growth algorithms considering vascular complexity and physiological variations.

Findings

Arterial structures are stable under physiological parameter variations.

Optimal junction exponents are sensitive to complexity and boundary conditions.

Full vascular complexity is crucial for understanding vasculature properties.

Abstract

We examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization (SALVO) algorithm, which we have previously used to reproduce features of cardiac and cerebral vasculatures. Fundamental biophysical understanding of complex vascular structure has applications to modelling of cardiovascular diseases, and for improved representations of vasculatures in large artificial tissues. In order to progress in-silico methods for growing arterial networks, we need to understand the stability of computational arterial growth algorithms to complexity, variations in physiological parameters such as tissue demand, and underlying assumptions regarding the value of junction exponents. We determine the globally optimal structure of two-dimensional arterial trees; analysing sensitivity of tree morphology and optimal bifurcation exponent to physiological parameters. We find that, for physiologically relevant simulation parameters, arterial structure is stable, whereas optimal junction exponents vary. We conclude that the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. These results are important for establishing that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while identifying that optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are sensitive to complexity and the boundary conditions dictated by organs.