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Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability

Here we developed a spatio-temporal bone remodeling model to simulate the action of Basic Multicelluar Units (BMUs). This model is based on two major extensions of a temporal-only bone cell population model (BCPM). First, the differentiation into mature resorbing osteoclasts and mature forming osteo...

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Autores principales: Calvo-Gallego, José Luis, Manchado-Morales, Pablo, Pivonka, Peter, Martínez-Reina, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027742/
https://www.ncbi.nlm.nih.gov/pubmed/36959906
http://dx.doi.org/10.3389/fbioe.2023.1060158
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author Calvo-Gallego, José Luis
Manchado-Morales, Pablo
Pivonka, Peter
Martínez-Reina, Javier
author_facet Calvo-Gallego, José Luis
Manchado-Morales, Pablo
Pivonka, Peter
Martínez-Reina, Javier
author_sort Calvo-Gallego, José Luis
collection PubMed
description Here we developed a spatio-temporal bone remodeling model to simulate the action of Basic Multicelluar Units (BMUs). This model is based on two major extensions of a temporal-only bone cell population model (BCPM). First, the differentiation into mature resorbing osteoclasts and mature forming osteoblasts from their respective precursor cells was modelled as an intermittent process based on precursor cells availability. Second, the interaction between neighbouring BMUs was considered based on a “metabolic cost” argument which warrants that no new BMU will be activated in the neighbourhood of an existing BMU. With the proposed model we have simulated the phases of the remodelling process obtaining average periods similar to those found in the literature: resorption ( [Formula: see text] days)—reversal (∼8 days)—formation (∼65 days)—quiescence (560–600 days) and an average BMU activation frequency of ∼1.6 BMUs/year/mm(3). We further show here that the resorption and formation phases of the BMU become coordinated only by the presence of TGF-β (transforming growth factor β), i.e., a major coupling factor stored in the bone matrix. TGF-β is released through resorption so upregulating osteoclast apoptosis and accumulation of osteoblast precursors, i.e., facilitating the transition from the resorption to the formation phase at a given remodelling site. Finally, we demonstrate that this model can explain targeted bone remodelling as the BMUs are steered towards damaged bone areas in order to commence bone matrix repair.
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spelling pubmed-100277422023-03-22 Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability Calvo-Gallego, José Luis Manchado-Morales, Pablo Pivonka, Peter Martínez-Reina, Javier Front Bioeng Biotechnol Bioengineering and Biotechnology Here we developed a spatio-temporal bone remodeling model to simulate the action of Basic Multicelluar Units (BMUs). This model is based on two major extensions of a temporal-only bone cell population model (BCPM). First, the differentiation into mature resorbing osteoclasts and mature forming osteoblasts from their respective precursor cells was modelled as an intermittent process based on precursor cells availability. Second, the interaction between neighbouring BMUs was considered based on a “metabolic cost” argument which warrants that no new BMU will be activated in the neighbourhood of an existing BMU. With the proposed model we have simulated the phases of the remodelling process obtaining average periods similar to those found in the literature: resorption ( [Formula: see text] days)—reversal (∼8 days)—formation (∼65 days)—quiescence (560–600 days) and an average BMU activation frequency of ∼1.6 BMUs/year/mm(3). We further show here that the resorption and formation phases of the BMU become coordinated only by the presence of TGF-β (transforming growth factor β), i.e., a major coupling factor stored in the bone matrix. TGF-β is released through resorption so upregulating osteoclast apoptosis and accumulation of osteoblast precursors, i.e., facilitating the transition from the resorption to the formation phase at a given remodelling site. Finally, we demonstrate that this model can explain targeted bone remodelling as the BMUs are steered towards damaged bone areas in order to commence bone matrix repair. Frontiers Media S.A. 2023-03-07 /pmc/articles/PMC10027742/ /pubmed/36959906 http://dx.doi.org/10.3389/fbioe.2023.1060158 Text en Copyright © 2023 Calvo-Gallego, Manchado-Morales, Pivonka and Martínez-Reina. https://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
Calvo-Gallego, José Luis
Manchado-Morales, Pablo
Pivonka, Peter
Martínez-Reina, Javier
Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title_full Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title_fullStr Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title_full_unstemmed Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title_short Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
title_sort spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027742/
https://www.ncbi.nlm.nih.gov/pubmed/36959906
http://dx.doi.org/10.3389/fbioe.2023.1060158
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