Key result
The SALVO computational algorithm demonstrated that the optimal bifurcation exponent of arterial trees is significantly affected by vascular network complexity and physiological boundary conditions.
Why the study?
A deeper understanding of the stability of computational arterial growth algorithms to complexity, variations in physiological parameters, and junction exponent assumptions was needed to advance computational methods for growing arterial networks.
Computational modeling demonstrates that the optimal bifurcation exponent in arterial networks is affected by vascular complexity and physiological boundary conditions, deviating from the classic Murray's law.
Challenges Murray's law assumptions in vascular modeling; leaves open optimal exponents for hemodynamic research.
We examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization algorithm, a computational method which we have previously used to reproduce features of cardiac and cerebral vasculatures. In order to progress computational methods for growing arterial networks, deeper understanding of the stability of computational arterial growth algorithms to complexity, variations in physiological parameters (such as metabolic costs for maintaining and pumping blood), and underlying assumptions regarding the value of junction exponents is needed. We determine the globally optimal structure of two-dimensional arterial trees; analysing how physiological parameters affect tree morphology and optimal bifurcation exponent. We find that considering the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. We conclude that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are affected by the complexity of vascular networks and the boundary conditions dictated by organs.
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Keelan et al. (2021) studied Vascular network optimization. Simulated Annealing Vascular Optimization (SALVO) algorithm was evaluated on Optimal bifurcation exponent (γopt). The SALVO computational algorithm demonstrated that the optimal bifurcation exponent of arterial trees is significantly affected by vascular network complexity and physiological boundary conditions.
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