ABSTRACT Diabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies. One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate "online" plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements from compartments alternative to blood. Current commercially available CGMS are minimally invasive and offer an estimation of plasma glucose from measurements in the interstitial fluid CGMS is a key component of the technical approach to build the artificial pancreas, aiming at closing the loop in combination with an insulin pump. Yet, the accuracy of current CGMS is still poor and it may partly depend on low performance of the implemented Calibration Algorithm (CA). In addition, the sensor-to-patient sensitivity is different between patients and also for the same patient in time. It is clear, then, that the development of new efficient calibration algorithms for CGMS is an interesting and challenging problem. The indirect measurement of plasma glucose through interstitial glucose is a main confounder of CGMS accuracy. Many components take part in the glucose transport dynamics. Indeed, physiology might suggest the existence of different local behaviors in the glucose transport process. For this reason, local modeling techniques may be the best option for the structure of the desired CA. Thus, similar input samples are represented by the same local model. The integration of all of them considering the input regions where they are valid is the final model of the whole data set. Clustering is t / Barceló Rico, F. (2012). Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17173 / Palancia

Cells adhere to each other and to the extracellular matrix (ECM) through protein molecules on the surface of the cells. The breaking and forming of adhesive bonds, a process critical in cancer invasion and metastasis, can be influenced by the mutation of cancer cells. Several molecules have been reported to play a crucial role in cellular adhesion and proliferation, and eventually in cancer progression, with TGF-β being one of the most important. In this thesis, we propose a general framework to model cancer cells movement and aggregation, in response to nonlocal social interactions (that is, attraction towards neighbours that are far away, repulsion from those that are near by, and alignment with neighbours at intermediate distances), as well as other molecules' effect, e.g., TGF-β. We develop nonlocal mathematical models describing cancer invasion and metastasis as a result of integrin-controlled cell-cell adhesion and cell-matrix adhesion, for two cancer cell populations with different levels of mutation. The models consist of nonlinear partial differential equations, describing the dynamics of cancer cells and TGF-β dynamics, coupled with nonlinear ordinary differential equations describing the ECM and integrins dynamics. We study our models analytically and numerically, and we demostrate a wide range of spatiotemporal patterns. We investigate the effect of mutation and TGF-β concentration on the speed on cancer spread, as well as the effect of nonlocal interactions on cancer cells' speed and turning behaviour.