Optimizing the dynamics of bone turnover with genetic algorithm

Abstract

Control systems and the modeling strategies are not only limited to engineering problems. These approaches can be used in the field of bio-mathematics as well and modern studies have promoted this approach to a great extent. The computational modeling and simulation of bone metastasis is painful yet critical after cancer invades the body. This vicious cycle is complex, and several research centers worldwide are devoted to understanding the dynamics and setting up a treatment strategy for this life-threatening behavior of cancer. Cancerous cells activation and the corresponding process of metastasis is reported to boost during the periodic waves of COVID-19, due to the inflammatory nature of the infection associated with SARS-2 and its variants. The bone cells are comprised of two types of cells responsible for bone formation and resorption. The computational framework of such cells, in spatial form, can help the researchers forecast the bone dynamics in a robust manner where the impact of cancer is incorporated into the computational model as a source of perturbation. A series of computational models are presented to explore the complex behavior of bone metastasis with COVID-19 induced infection. The finite difference algorithm is used to simulate the nonlinear computational model. The results obtained are in close agreement with the experimental findings. The computational results can help explore the vicious cycle’s fate and help set up control strategies through drug therapies.

A review of mathematical modeling of bone remodeling from a systems biology perspective

“…Unique among other related reviews (Gerhard et al, 2009;Geris et al, 2009;Pivonka and Komarova, 2010;Webster and Müller, 2011;Riggs and Cremers, 2019;Coelho et al, 2020), our review analyzes the mathematical forms used to represent the physiological processes of bone remodeling, highlights important local and systemic biological features found in mathematical models, and synthesizes these into comprehensive tables that should be useful to others interested in building or adapting such models (Tables 1-7). Spatiotemporal biochemical models (van Oers et al, 2008;Ryser et al, 2009;Ayati et al, 2010;Ryser et al, 2010;Buenzli et al, 2011;Buenzli et al, 2012a;Graham and Ayati, 2012;Ryser et al, 2012;Araujo et al, 2014;Buenzli et al, 2014;Buenzli, 2015;Lerebours et al, 2016;Ryser and Murgas, 2017;Arias et al, 2018;Peyroteo et al, 2019;Kameo et al, 2020;Taylor-King et al, 2020;Baldonedo et al, 2021;Calvo-Gallego et al, 2023;Idrees and Sohail, 2023) are categorized in Table 1. Temporal biochemical models are organized based on which of two prevailing mathematical formulations are used to describe bone cell population dynamics and their biochemical signaling dynamics in the BMU.…”