Insulin is an essential hormone that regulates nutrient homeostasis. Insufficient insulin results in diabetes, one of the most prevalent and costly diseases. Although the primary actions of insulin are to induce glucose uptake and metabolism in distant tissues, including muscle, fat and liver, the insulin secreting pancreatic beta-cells contain a high number of insulin receptors and known to respond to the hormone. On a minute-to-minute time-scale, insulin has been reported to have negative feedback effects on its own secretion, and we have data suggesting that the actions of insulin may be context-dependent, potentially depending on the ambient glucose levels (which are primarily controlled by glucose). Insulin has also been reported to have positive effects on its own synthesis and on the survival of the beta-cells over a timescale of months. Within beta-cells, insulin production is inherently stressful and exerts a negative effect on beta-cell proliferation that is most pronounced at a young age. We have also recently found that single beta-cells can exist in ‘bursting’ states of elevated insulin production that account for a significant proportion of the previous described heterogeneity in this cell type. Thus, using a variety of experimental approaches, we seek to understand context-dependent insulin feedback signalling on single beta-cells and their collective populations and we are interested in collaborating to build quantitative and testable models that could be used to explain the pathogenesis of diabetes. We also interested in expanding models to include other tissues and other soluble factors that are also relevant in nutrient homeostasis and diabetes.

My research program is motivated by fascination with bird flight. My laboratory group uses a multi-disciplinary approach that includes biomechanics, physiology, and neuroscience to examine flight ability. Our current research is organized around two topics: 1) how birds morph their wings and what benefits this provides; and 2) how optic flow signals are encoded in the avian brain and used to guide their flight. As we gain understanding of flight mechanisms, we further endeavor to apply comparative approaches that provide deeper insight into avian ecology and evolution.

STORM is a super-resolution technique that uses photoswitchable fluorophores to achieve resolutions at or below 20nm. A downside of STORM is the possibility of recording several blinks from one fluorophore, affecting the estimation of the number of molecules detected in the image. I constructed a mathematical model to identify unique fluorophores in STORM images by independently using the localization and the time series of the observations. The temporal sequence is described with a Markov chain approach and their spatial distribution with a Gaussian mixture model. To estimate the parameter values, I implemented a maximum likelihood procedure which requires a mixed optimization. I have tested my protocol in simulated data and I will use it to improve STORM images of B-cell surface receptors. B-cell receptors distribution on the membrane has been related to B-cell activation. This model will enhance a microscopy technique that is already widely used in biological applications and will allow to deeper analyze immune cells signaling.

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