Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Review Diabetes Technology Theory: Metabolic Models, in Silico Trials, and Algorithms
Ali Cinar et al
Abstract
Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.
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Journal of The Royal Society Interface: Personalizing computational models to construct medical digital twins
Adam Knapp et al
Abstract
Digital twin technology, originally developed for engineering, is being adapted to biomedicine and healthcare. A key challenge in this process is dynamically calibrating computational models to individual patients using data collected over time. This calibration is vital for improving model-based predictions and enabling personalized medicine. Biomedical models are often complex, incorporating multiple scales of biology and both stochastic and spatially heterogeneous elements. Agent-based models, which simulate autonomous agents, such as cells, are commonly used to capture how local interactions affect system-level behaviour. However, no standard personalization methods exist for these models. The main challenge is bridging the gap between clinically measurable macrostates (e.g. blood pressure and heart rate) and the detailed microstate data (e.g. cellular processes) needed to run the model. In this article, we propose an algorithm that applies the ensemble Kalman filter, a classic data-assimilation technique, at the macrostate level. We then link the Kalman update at the macrostate to corresponding updates at the microstate level, ensuring that the resulting microstates are compatible with the desired macrostates and consistent with the model's dynamics. This approach improves the personalization of complex biomedical models and enhances model-based forecasts for individual patients.
