Modelling Autoregulation in the Kidney
Chronic kidney disease includes conditions that damage the kidneys and decrease their ability to maintain homeostasis in the body. As kidney disease progresses, waste products can rise to high levels in the blood and cause illness and complications like high blood pressure, anemia, weak bones, poor nutritional health, and nerve damage. Also, kidney disease increases the risk of developing heart and blood vessel disease, deterioration which can occur over a long period of time. Chronic kidney disease may be caused by diabetes, high blood pressure and other disorders. When kidney disease progresses, it may eventually lead to kidney failure, which requires dialysis or a kidney transplant to survive. There is therefore great emphasis placed on the understanding of factors that increase the risk of kidney disease and how blood is distributed and autoregulated throughout the kidney, enabling proper filtration.
The current research is focused on modelling the kidney on multiple levels, from the functionality of the whole organ down to autoregulation in the nephron, the filtration unit of the kidney. There are approximately one million nephrons in each human kidney, each being an independent entity capable of producing urine via plasma filtration.
The filtration and excretion of metabolic waste is a major function of the kidney that is closely related to its' other important task, maintaining desired concentrations of key constituents in bodily fluids, namely sodium. The filtration, reabsorption, and excretion process of the nephron is elaborate, and there is a large amount of evidence that suggests chaotic interactions between neighboring nephrons, which adds another degree of complexity to the model. In gaining a greater understanding of the mechanisms governing this balance, we hope to develop novel diagnostic and treatment approaches that are more effective than current medical practices.
The Quantitative Kidney Database is a project that has the full support of our group, and it is a rapidly growing resource of experimental data and modeling parameters.
This project is under the supervision of Professor Tim David. Postgraduate students working on the project include Nicole Kleinstreuer and Scott Graybill. This work is being conducted in cooperation with Dr. Mike Plank and Dr. Alex James of the Mathematics department and Professor Zoltan Endre, a nephrologist from the Christchurch School of Medicine and Health Sciences.