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Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?

Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the...

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Autores principales: Tekin, Elif, Hunt, David, Newberry, Mitchell G., Savage, Van M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5130167/
https://www.ncbi.nlm.nih.gov/pubmed/27902691
http://dx.doi.org/10.1371/journal.pcbi.1005223
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author Tekin, Elif
Hunt, David
Newberry, Mitchell G.
Savage, Van M.
author_facet Tekin, Elif
Hunt, David
Newberry, Mitchell G.
Savage, Van M.
author_sort Tekin, Elif
collection PubMed
description Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the human head and torso and of a mouse lung based on three-dimensional images processed via our software Angicart. In contrast to modern allometric theories, we find systematic patterns of asymmetry in vascular branching, potentially explaining previously documented mismatches between predictions (power-law or concave curvature) and observed empirical data (convex curvature) for the allometric scaling of metabolic rate. To examine why these systematic asymmetries in vascular branching might arise, we construct a mathematical framework to derive predictions based on local, junction-level optimality principles that have been proposed to be favored in the course of natural selection and development. The two most commonly used principles are material-cost optimizations (construction materials or blood volume) and optimization of efficient flow via minimization of power loss. We show that material-cost optimization solutions match with distributions for asymmetric branching across the whole network but do not match well for individual junctions. Consequently, we also explore random branching that is constrained at scales that range from local (junction-level) to global (whole network). We find that material-cost optimizations are the strongest predictor of vascular branching in the human head and torso, whereas locally or intermediately constrained random branching is comparable to material-cost optimizations for the mouse lung. These differences could be attributable to developmentally-programmed local branching for larger vessels and constrained random branching for smaller vessels.
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spelling pubmed-51301672016-12-15 Do Vascular Networks Branch Optimally or Randomly across Spatial Scales? Tekin, Elif Hunt, David Newberry, Mitchell G. Savage, Van M. PLoS Comput Biol Research Article Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the human head and torso and of a mouse lung based on three-dimensional images processed via our software Angicart. In contrast to modern allometric theories, we find systematic patterns of asymmetry in vascular branching, potentially explaining previously documented mismatches between predictions (power-law or concave curvature) and observed empirical data (convex curvature) for the allometric scaling of metabolic rate. To examine why these systematic asymmetries in vascular branching might arise, we construct a mathematical framework to derive predictions based on local, junction-level optimality principles that have been proposed to be favored in the course of natural selection and development. The two most commonly used principles are material-cost optimizations (construction materials or blood volume) and optimization of efficient flow via minimization of power loss. We show that material-cost optimization solutions match with distributions for asymmetric branching across the whole network but do not match well for individual junctions. Consequently, we also explore random branching that is constrained at scales that range from local (junction-level) to global (whole network). We find that material-cost optimizations are the strongest predictor of vascular branching in the human head and torso, whereas locally or intermediately constrained random branching is comparable to material-cost optimizations for the mouse lung. These differences could be attributable to developmentally-programmed local branching for larger vessels and constrained random branching for smaller vessels. Public Library of Science 2016-11-30 /pmc/articles/PMC5130167/ /pubmed/27902691 http://dx.doi.org/10.1371/journal.pcbi.1005223 Text en © 2016 Tekin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tekin, Elif
Hunt, David
Newberry, Mitchell G.
Savage, Van M.
Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title_full Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title_fullStr Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title_full_unstemmed Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title_short Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?
title_sort do vascular networks branch optimally or randomly across spatial scales?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5130167/
https://www.ncbi.nlm.nih.gov/pubmed/27902691
http://dx.doi.org/10.1371/journal.pcbi.1005223
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