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Resilience of three-dimensional sinusoidal networks in liver tissue

Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in...

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Autores principales: Karschau, Jens, Scholich, André, Wise, Jonathan, Morales-Navarrete, Hernán, Kalaidzidis, Yannis, Zerial, Marino, Friedrich, Benjamin M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351228/
https://www.ncbi.nlm.nih.gov/pubmed/32598356
http://dx.doi.org/10.1371/journal.pcbi.1007965
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author Karschau, Jens
Scholich, André
Wise, Jonathan
Morales-Navarrete, Hernán
Kalaidzidis, Yannis
Zerial, Marino
Friedrich, Benjamin M.
author_facet Karschau, Jens
Scholich, André
Wise, Jonathan
Morales-Navarrete, Hernán
Kalaidzidis, Yannis
Zerial, Marino
Friedrich, Benjamin M.
author_sort Karschau, Jens
collection PubMed
description Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow—a feature known for hierarchical networks, but unexpected for plexus-like networks. We quantify network resilience in terms of a permeability-at-risk, i.e., permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver.
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spelling pubmed-73512282020-07-22 Resilience of three-dimensional sinusoidal networks in liver tissue Karschau, Jens Scholich, André Wise, Jonathan Morales-Navarrete, Hernán Kalaidzidis, Yannis Zerial, Marino Friedrich, Benjamin M. PLoS Comput Biol Research Article Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow—a feature known for hierarchical networks, but unexpected for plexus-like networks. We quantify network resilience in terms of a permeability-at-risk, i.e., permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver. Public Library of Science 2020-06-29 /pmc/articles/PMC7351228/ /pubmed/32598356 http://dx.doi.org/10.1371/journal.pcbi.1007965 Text en © 2020 Karschau 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
Karschau, Jens
Scholich, André
Wise, Jonathan
Morales-Navarrete, Hernán
Kalaidzidis, Yannis
Zerial, Marino
Friedrich, Benjamin M.
Resilience of three-dimensional sinusoidal networks in liver tissue
title Resilience of three-dimensional sinusoidal networks in liver tissue
title_full Resilience of three-dimensional sinusoidal networks in liver tissue
title_fullStr Resilience of three-dimensional sinusoidal networks in liver tissue
title_full_unstemmed Resilience of three-dimensional sinusoidal networks in liver tissue
title_short Resilience of three-dimensional sinusoidal networks in liver tissue
title_sort resilience of three-dimensional sinusoidal networks in liver tissue
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351228/
https://www.ncbi.nlm.nih.gov/pubmed/32598356
http://dx.doi.org/10.1371/journal.pcbi.1007965
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