State and parameter estimation in dynamical metabolic models for personalized model-based management of diabetes

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In this research project a model-based procedure for personalized diagnosis of patients with diabetes mellitus is developed which estimates progress of blood glucose based on a model of glucose insulin metabolism. The model should be identified using established blood glucose measurements and novel sensors. Analysis of controllability and observability of states and parameters of that system follows. Required parameter sets shall be fitted to each individual in order to obtain better predictions superior to generic model approaches. Abnormal changes in the metabolic system, which could arise in diabetes mellitus, can be individually characterized by a change of these parameters. The gained methods can assist users in their diagnosis and treatment.Diabetes mellitus is a chronic metabolic disease. It results from the body's inability to produce and/ or use insulin. Regardless of the specific type of diabetes affected people need a lifelong insulin therapy.Therapy of diabetes tends to support the disturbed physiologic control of glucose insulin homeostasis by an artificial feedback control. As sensor a patient can measure glucose levels in blood or in subcutaneous tissue. As actuator insulin can be injected. Feedback control is established by the patient oneself after clinically instruction.Metabolic processes are, like technical systems, describable by the use of differential equations. Therefore, it can be assumed that diagnosis and treatment of diabetes mellitus benefits from a model-based proceeding.Previous approaches to system identification consider individual condition of patients only insufficient, because of huge variabilities in metabolic behavior between different persons. Also good standard models describe these processes only for mean values gathered from collectives. Actually physicians try to tackle these problems by developing 'personalized medicine'. That means for a model-based approach besides a fine-grained model, to design an efficient and personalized model identification system, which needs few measurement points. The focus is on novel, continuously measuring sensors which make it possible to capture the dynamics in the waveforms for the first time.It should be possible to derive diagnostic information that are much better in quality superior to considering only rigid limits.This can be done by adapting the insulin therapy based on the current condition of the patient or to optimize number of glucose measurements. Taking into account disturbances such as meals or physical activity a prediction of future blood glucose levels is possible.