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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...

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Autores principales: Taylor-King, Jake P., Buenzli, Pascal R., Chapman, S. Jon, Lynch, Conor C., Basanta, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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.
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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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