Modeling And Estimation For The Renal System

Understanding how a therapy will impact the injured kidney before being administered would be an asset to the clinical world. The work in this thesis advances the field of mathematical modeling of the kidneys to aid in this cause. The objectives of this work are threefold: 1) to develop and personalize a model to specific patients in different diseased states, via parameter estimation, in order to test therapeutic trajectories, 2) to use parameter estimation to understand the cause of different kidney diseases, differentiate between potential kidney diseases, and facilitate targeted therapies, and 3) to push forward the understanding of kidney physiology via physiology-based mathematical modeling techniques. To accomplish these objectives, we have developed two models of the kidneys: 1) a broad, steady-state, closed-loop model of the entire kidney with human physiologic parameters, and 2) a detailed, dynamic model of the proximal tubule, an important part of kidney, with rat physiologic parameters.