This project seeks to integrate computational methods, including mathematical modeling and image analysis techniques, with quantitative experimental approaches to understand complex cellular behavior. In particular, we investigate cellular processes, such as cell fate decisions, polarity establishment, and gradient sensing, that are coordinated by intracellular signaling networks. The goal of our research is to understand how these networks function as integrated systems. Mathematical modeling is needed to understand the feedback and feed forward regulation that coordinates the spatiotemporal dynamics of signaling pathways, and computational image analysis is needed to extract the quantitative information from live-cell images that is required to inform and validate the models. The primary model organism we use to study cell signaling is the yeast Saccharomyces cerevisiae. Our investigations combine microfluidic technology with live-cell microscopy to observe cellular behavior in well-controlled environments. This work is performed together with the labs of our longstanding collaborators Drs. Beverly Errede, Daniel Lew and Henrik Dohlman. Established projects in the lab include: 1) understanding molecular mechanisms involved in cell polarity and gradient sensing and 2) identifying signaling motifs that dynamically regulate gene expression and fate decisions. An exciting new direction for the lab is to investigate the effects of aging on cell signaling. My lab is also involved in collaborative projects to investigate cell signaling in mammalian cells and how the dysregulation of these pathways leads to human disease. Current projects in the lab include: 1) a longstanding collaboration with Dr. Richard Boucher to understand how purinergic signaling maintains airway surface liquid homeostasis and is dysregulated in cystic fibrosis, 2) a new project with our established collaborator Dr. Klaus Hahn to understand mechanisms that coordinate signaling during phagocytosis and 3) a new project with Dr. Ned Sharpless to understand the mechanisms that regulate cell differentiation during hematopoiesis. The unifying theme of all these projects is the use of mathematical modeling to understand how feedback and feed forward regulation generates coordinated spatiotemporal behavior. The ultimate goal of our investigations is to generate truly predictive models of in vivo cellular processes that can be applied to the rationale design of new therapeutic strategies. !
The goal of this project is to combine mathematical modeling and computational image analysis with quantitative experimental approaches to understand the feedback and feed forward loops that regulate intracellular signaling networks. In human cells, dysregulation of these networks leads to many diseases, including cancer and heart disease. The ultimate goal of this project is the development of predictive models of in vivo cellular processes that can be used in the rationale design of therapeutic strategies for treating disease.
