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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7351228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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