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A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model
A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model
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A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model
A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model

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A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model
A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model
Journal Article

A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova’s model

2023
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Overview
This paper aims to construct a general framework of coupling tumor–bone remodeling processes in order to produce plausible outcomes of the effects of tumors on the number of osteoclasts, osteoblasts, and the frequency of the bone turnover cycle. In this document, Komarova’s model has been extended to include the effect of tumors on the bone remodeling processes. Thus, we explored three alternatives for coupling tumor presence into Komarova’s model: first, using a “damage” parameter that depends on the tumor cell concentration. A second model follows the original structure of Komarova, including the tumor presence in those equations powered up to a new parameter, called the paracrine effect of the tumor on osteoclasts and osteoblasts; the last model is replicated from Ayati and collaborators in which the impact of the tumor is included into the paracrine parameters. Through the models, we studied their stability and considered some examples that can reproduce the tumor effects seen in clinic and experimentally. Therefore, this paper has three parts: the exposition of the three models, the results and discussion (where we explore some aspects and examples of the solution of the models), and the conclusion.