Calcium (Ca) homeostasis is the maintenance of a stable plasma Ca concentration in the human body in the presence of Ca variability in the physiological environment (e.g. by ingestion and/or excretion). For normal physiological function, the total plasma Ca concentration must be maintained within a very narrow range (2.2-2.4mM). Meeting such stringent requirements is the task of a regulatory system that employs parathyroid hormone (PTH) and calcitriol (CTL) to regulate Ca flux between the plasma and the kidneys, intestines and bones. On the other hand, plasma phosphate control is less tightly, but simultaneously, regulated via the same hormonal actions. Chronic imbalances in plasma Ca levels are associated with disorders of the regulatory organs, which cause abnormal hormonal secretion and activity. These changes in hormonal activity may lead to long-term problems, such as, osteoporosis (increased loss of bone mineral density), which arises from primary hyperparathyroidism (PHPT) – hyper secretion of PTH. Existing in silico models of Ca homeostasis in humans are often cast in the form of a single monolithic system of differential equations and are not easily amenable to the sort of tractable quantitative analysis from which one can acquire useful fundamental insight. In this research, the regulatory systems of plasma Ca and plasma phosphate are represented as an engineering control system where the physiological sub-processes are mapped onto corresponding block components (sensor, controller, actuator and process) and underlying mechanisms are represented by differential equations. Following validation of the overall model, Ca-related pathologies are successfully simulated through induced defects in the control system components. A systematic approach is used to differentiate PHPT from other diseases with similar pathophysiologies based on the unique hormone/ion responses to short-term Ca disturbance in each pathology model. Additionally, based on the changes in intrinsic parameters associated with PTG behavior, the extent of PHPT progression can be predicted and the enlarged gland size estimated a priori. Finally, process systems engineering methods are used to explore therapeutic intervention in two Ca-related pathologies: Primary (PHPT) and Secondary (SHPT) Hyperparathyroidism. Through parametric sensitivity analysis and parameter space exploration, the calcium-sensing receptor (sensor) is identified as a target site in both diseases and the extent of potential improvement is determined across the spectrum of severity of PHPT. The findings are validated against existing drug therapy, leading to a method of predicting drug dosage for a given stage of PHPT. Model Predictive Control is used in drug therapy in SHPT to customize the drug dosage for individual patients given the desired PTH outcome, and drug administration constraints. / Ph. D.