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Modeling Osteocyte Network Formation: Healthy and Cancerous Environments
Advanced cancers, such as prostate and breast cancers, commonly metastasize to bone. In the bone matrix, dendritic osteocytes form a spatial network allowing communication between osteocytes and the osteoblasts located on the bone surface. This communication network facilitates coordinated bone remo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387425/ https://www.ncbi.nlm.nih.gov/pubmed/32793566 http://dx.doi.org/10.3389/fbioe.2020.00757 |
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author | Taylor-King, Jake P. Buenzli, Pascal R. Chapman, S. Jon Lynch, Conor C. Basanta, David |
author_facet | Taylor-King, Jake P. Buenzli, Pascal R. Chapman, S. Jon Lynch, Conor C. Basanta, David |
author_sort | Taylor-King, Jake P. |
collection | PubMed |
description | Advanced cancers, such as prostate and breast cancers, commonly metastasize to bone. In the bone matrix, dendritic osteocytes form a spatial network allowing communication between osteocytes and the osteoblasts located on the bone surface. This communication network facilitates coordinated bone remodeling. In the presence of a cancerous microenvironment, the topology of this network changes. In those situations, osteocytes often appear to be either overdifferentiated (i.e., there are more dendrites than healthy bone) or underdeveloped (i.e., dendrites do not fully form). In addition to structural changes, histological sections from metastatic breast cancer xenografted mice show that number of osteocytes per unit area is different between healthy bone and cancerous bone. We present a stochastic agent-based model for bone formation incorporating osteoblasts and osteocytes that allows us to probe both network structure and density of osteocytes in bone. Our model both allows for the simulation of our spatial network model and analysis of mean-field equations in the form of integro-partial differential equations. We considered variations of our model to study specific physiological hypotheses related to osteoblast differentiation; for example predicting how changing biological parameters, such as rates of bone secretion, rates of cancer formation, and rates of osteoblast differentiation can allow for qualitatively different network topologies. We then used our model to explore how commonly applied therapies such as bisphosphonates (e.g., zoledronic acid) impact osteocyte network formation. |
format | Online Article Text |
id | pubmed-7387425 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73874252020-08-12 Modeling Osteocyte Network Formation: Healthy and Cancerous Environments Taylor-King, Jake P. Buenzli, Pascal R. Chapman, S. Jon Lynch, Conor C. Basanta, David Front Bioeng Biotechnol Bioengineering and Biotechnology Advanced cancers, such as prostate and breast cancers, commonly metastasize to bone. In the bone matrix, dendritic osteocytes form a spatial network allowing communication between osteocytes and the osteoblasts located on the bone surface. This communication network facilitates coordinated bone remodeling. In the presence of a cancerous microenvironment, the topology of this network changes. In those situations, osteocytes often appear to be either overdifferentiated (i.e., there are more dendrites than healthy bone) or underdeveloped (i.e., dendrites do not fully form). In addition to structural changes, histological sections from metastatic breast cancer xenografted mice show that number of osteocytes per unit area is different between healthy bone and cancerous bone. We present a stochastic agent-based model for bone formation incorporating osteoblasts and osteocytes that allows us to probe both network structure and density of osteocytes in bone. Our model both allows for the simulation of our spatial network model and analysis of mean-field equations in the form of integro-partial differential equations. We considered variations of our model to study specific physiological hypotheses related to osteoblast differentiation; for example predicting how changing biological parameters, such as rates of bone secretion, rates of cancer formation, and rates of osteoblast differentiation can allow for qualitatively different network topologies. We then used our model to explore how commonly applied therapies such as bisphosphonates (e.g., zoledronic acid) impact osteocyte network formation. Frontiers Media S.A. 2020-07-22 /pmc/articles/PMC7387425/ /pubmed/32793566 http://dx.doi.org/10.3389/fbioe.2020.00757 Text en Copyright © 2020 Taylor-King, Buenzli, Chapman, Lynch and Basanta. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Taylor-King, Jake P. Buenzli, Pascal R. Chapman, S. Jon Lynch, Conor C. Basanta, David Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title | Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title_full | Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title_fullStr | Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title_full_unstemmed | Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title_short | Modeling Osteocyte Network Formation: Healthy and Cancerous Environments |
title_sort | modeling osteocyte network formation: healthy and cancerous environments |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387425/ https://www.ncbi.nlm.nih.gov/pubmed/32793566 http://dx.doi.org/10.3389/fbioe.2020.00757 |
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