Advancing Machine Learning and Statistical Methodologies for Understanding Dynamics of Biological Systems
Modern imaging technologies enable us to monitor, for example, spatiotemporal dynamics of protein activities at the subcellular level, and the cross talk between thousands of individual neurons in freely moving animals. Interpretation of these complex dynamics in biological systems requires computational methods and modeling approaches. Our lab collaborates with cell biologists and neuroscientists to better understand the dynamic biological systems involved in human diseases, including cytoskeleton reorganization and nervous system. We integrate financial mathematics— originally developed for understanding economic systems—into our computational framework to analyze time-lapse images of live cells and tissues. Applying stochastic modeling approaches, we aim to uncover subcellular level signaling dynamics and information transmission between individual neurons.