Research in the laboratory utilizes a combination of quantitative experiments and mathematical modeling to study signaling dynamics and spatial cellular organization in both normal development and during cancer progression. Research focuses on three major related areas:
1. Self-organized developmental patterning. During development, the cells of the embryo create patterns of cell fates and gene expression. We recently showed that some of the earliest steps of the patterning can be recapitulated in vitro with human embryonic stem cells using micropatterning technologies. This system allows us to study development using a “bottom-up” approach, in which we attempt to recapitulate and manipulate the development of hESCs. We are now using this system to address a number of questions regarding early development. What are the physical mechanism by which extracellular signals spread and how do cells use these signals to self-organize into fate patterns? What is the relationship between the dynamics of signaling in an individual cell and the fate it ultimately adopts? These questions are difficult to address in intact embryos, particularly in mammalian embryos that develop in utero, however, the hESC system allows us to finely manipulate both physical and chemical variables and to directly observe a variety of signaling and fate reporters in individual cells. The wealth of data generated from these experiments is organized and extended using a mathematical modeling approach that determines the landscape of fates available to a cell during early development and the relationship between those fates.
2. Dynamic processing by signaling pathways. Molecular signaling pathways are not simple on/off switches but respond dynamically to ligand stimulation in a way that is critical for shaping cellular outcomes. While the biochemistry of many key signaling pathways has been elucidated in great detail, understanding of signaling dynamics remains limited. Work in the laboratory uses an automated microfluidic platform and live-cell signaling reporters to probe the dynamics of signaling pathways with a special focus on the TGF-beta pathway. This approach allows us to determine whether pathways are primed to respond to certain temporal stimuli while filtering out others. Experiments are complimented by creating data-driven mathematical models of the signaling pathways which enables us to design optimal protocols of stimulation for achieving particular outcomes.
3. Modeling the cancer microenvironment. Interactions between healthy and diseased cells play an important role in cancer progression, however, little is known about how the parameters of those interactions shape the response. Using micropatterning technologies, we will recreate the geometry of the tumor microenvironment for ovarian cancer and study how the relative position of different cell types impacts outcomes such as cell proliferation and migration. We will also utilize signaling reporters to the TGF-b and Wnt pathways (known to be important for the interaction between ovarian cancer cells and cancer associated fibroblasts) to understand the dynamics of how these cells communicate. Ultimately, we hope that by understand the interactions between these different types of cells, we will be able to engineer particular dynamic signaling perturbations that can disrupt support for the cancer cells without affecting interactions between the healthy cells