• Jakob Polcwiartek
In the field of biomedical engineering, physiological modelling of the glucose-insulin system may improve understanding of diabetes by e.g. estimating the insulin sensitivity in healthy individuals and patients with prediabetes or type 2 diabetes. The gold standard of measuring insulin sensitivity is the euglyceamic hyperinsulinemic clamp technique, which is rather tedious, invasive, and labour-intensive. A comparable alternative to this is the Bergman minimal model, assessing insulin sensitivity by the intravenous glucose tolerance test (IVGTT). This model has gained much attention in the literature and has been implemented as the PC application MINMOD Millennium, which unfortunately is limited to one optimisation algorithm almost without any level of customisation besides being rather outdated.

Therefore, this MSc Thesis aimed to re-engineer a version of MINMOD, with focus on providing more customisation of the optimisation process to estimate e.g. insulin sensitivity.

A nonlinear least squares (NLS) approach was used together with the Bergman minimal model to develop a PC application in MATLAB (version R2015a). Relevant IVGTT data from the literature was found, extracted, prepared, and implemented into the re-engineered version of MINMOD. This was presented in a graphical user interface (GUI), and the software development process was mainly based on the waterfall model and usability heuristics.

During the software development process, it was possible to re-engineer a version of MINMOD in an aesthetic and minimalistic GUI. Here, it was possible to customise the optimisation process regarding estimates of relevant parameters, including insulin sensitivity, of the Bergman minimal model. Overall, parameter estimates could be replicated and compared with other studies found in literature, validating the re-engineered version of MINMOD to some degree. However, more testing is required in order to obtain an even higher degree of validation.
Publication date7 Jun 2017
Number of pages100


ID: 259304654