Cell-based expression cloning for identification of polypeptides that hypersensitize mammalian cells to mitotic arrest

Microtubule inhibitors such as Vinblastine and Paclitaxel are chemotherapy agents that activate the mitotic spindle checkpoint, arresting cells in mitosis and leading to cell death. The pathways that connect mitotic arrest to cell death are not well characterized. We developed a mammalian cell-based cDNA cloning method to isolate proteins and protein fragments whose expression inhibits colony formation in the presence of microtubule inhibitors. Understanding how these proteins impact cellular responses to microtubule drugs will lead to better understanding of the biochemical pathways connecting mitotic arrest and cell death in mammalian cells and may provide novel targets that can enhance microtubule inhibitor-mediated chemotherapy

Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties

We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closed-loop insulin control is the uncertainty in those aspects of a patient's daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.Comment: Extended version of paper accepted at the 15th International Conference on Computational Methods in Systems Biolog