ArticlePLoS computational biology2020
Resilience of three-dimensional sinusoidal networks in liver tissue.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- Liver Innervation in Health and Disease: Neuroimmune-Neurovascular Interface and Future Therapeutic Implications.Biomedicines · 2025Review
- Virtual tissue microstructure reconstruction across species using generative deep learning.PloS one · 2024Article
- Tryptophan Prevents the Development of Non-Alcoholic Fatty Liver Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Myostatin Knockout Regulates Bile Acid Metabolism by Promoting Bile Acid Synthesis in Cattle.Animals : an open access journal from MDPI · 2022Article
- Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue.Journal of pathology informatics · 2022Article
- Quantification of nematic cell polarity in three-dimensional tissues.PLoS computational biology · 2020Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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