The thyroid, the largest gland in the endocrine system, secretes hormones that regulate homeostatic functions within the body and promote normal growth and development. In recent years, great strides have been made in identifying chemical signal pathways within the thyroid. Based upon recent molecular-level discoveries and clinical observations, this team has engineered a detailed computational model of the thyroid gland. Motivated by this model's development and confirmation, the main goal of the proposed research is to understand the thyroid's fundamental dynamics and to translate this understanding into therapeutic protocols for treating thyroid dysfunction. Examples of the proposed research include: 1) Understanding the dynamic of thyroid enlargement (goiter), 2) Analyzing the effects of drugs and environmental contaminants on thyroid hormone secretion, and 3) Improving radioactive iodine treatment of Grave's disease. The intellectual merit of this work is centered upon coordination of computational modeling, dynamical systems analysis, and clinical experience. The broader impact of the proposed research has short and long term implications: (i) improving diagnosis power (early intervention), (ii) improving treatment protocols (Grave's disease and hormone replacement), and (iii) enabling enhanced testing/screening of pharmaceuticals (mimic, enhance, diminish or block hormone actions).
The thyroid, the largest gland in the endocrine system, secretes hormones that regulate homeostatic functions within the body and promote normal growth and development. In recent years, great strides have been made in identifying chemical signal pathways within the thyroid. However, treatment protocols, which rely on statistical analysis of clinical studies, appear to have limited effectiveness in individual applications. While clinical studies are ultimate arbiters of a protocol’s success, they may be of less value in developing protocols. Clinical studies require careful planning, evolve on long time scales, are constrained by the capabilities of instrumentation, have limited feasibility involving human subjects, and are of little use in studying behavior under toxic conditions. We have developed high-fidelity computational models of the thyroid and of parathyroid glands to answer "what if" questions whose answers can then lead to improved understanding of the underlying behaviors. This model has been used to develop a new method for determining the appropriate dose of radioactive iodine in the treatment of Graves’ disease. Similar system-level modeling of bio-systems in the human body has also been implemented to model characteristics of the parathyroid glands and anemia of chronic kidney disease. The new models are currently being used to gain new insight into bio-systems complicated dynamics, for example, to explore engineering solutions to the design of anemia management protocols in chronic kidney disease.
