Multi Agent Functional Bone Simulation: A theoretical study

• Autor(a) principal: Silvestre, Inês Tomás Rosa da Cruz
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/146165
• topic: Bone Metabolism; Multiagents; Stochastic; Osteoporosis; Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias

In 200 A.D., Galen described bones as the fundamental system of body protection. Bones are highly dynamic, in constant renovation to preserve their properties. Understanding bone metabolism has become a relevant area of research. The most common bone disease is osteoporosis and is characterized by low bone mass and microarchitecture disturbances. The major consequence of osteoporosis are fragility fractures (fractures that occur with low impact trauma). Osteoporosis causes more than 8.9 million fractures each year worldwide. Several therapies are effective in preventing fractures and treating osteoporosis. However, there is an enormous difficulty in predicting osteoporosis related fractures and understanding who needs these therapies in order to prevent bone loss. In practical clinic, it becomes essential to have a model describing the bone remodeling process and the impact of the different cellular mediators in the bone metabolism. Although some mathematical models already describe the variation of the bone cells, they do it in a continuous and deterministic way with few cellular mediators. Many studies and experimental results have shown that cellular metabolism and birth-and-death processes in population dynamics are stochastic. Furthermore, mathematical models are reliable on describing a macro level, whereas multiagent simulation models are used to link micro and macro perspectives. In this thesis we have developed a multiagent stochastic model that simulates a timeline remodeling cycle. Our simulator reproduces the homeostatic process of remodeling with the different phases of it, which is time consistent with the real biological process. Our model includes the most relevant cellular mediators in the bone metabolism. Our model demonstrated to have great sensibility to predict bone loss caused by some chronic diseases such hyper and hypoparathyroidism, and excess of glucocorticoids, and also to the most known causes of osteoporosis: estrogen or vitamin D deficiency. Overall, this model provides a deeper understanding about bone metabolism and the pathologies associated with it.