Welcome to the Sorger Lab
Our lab applies experimental and computational methods to the analysis of mechanical and regulatory processes controlling eucaryotic cell division. We seek to construct data-driven, systems-wide models of cellular function that nonetheless contain detailed mechanistic information on the activities of individual proteins.
Our research focuses on the microtubule-based machines that segregate chromosomes during mitosis and on the signal transduction networks that regulate cell proliferation and death. Defects in these pathways are known to predispose cells to oncogenic transformation and we are actively developing a pharmacological approach in which disease and therapy are viewed through the prism of quantitative numerical models. The lab is focused on two main areas research.
One long term goals of lab’s research is to identify molecular lesions that cause genomic instability and promote tumorigenesis, to determine their frequency in normal and cancerous cells, and to develop improved means to kill diseased tissues. When healthy cells divide, chromosomes are partitioned among daughter cells with great fidelity. However, in cancer cells, this fidelity is lost and cells exhibit genomic instability. It is thought that genomic instability plays an important role in accumulation of the multiplicity of genetic mutations characterizing cancer in humans. Approaches in the lab include quantitative single-cell measurement, genetically engineered mice and quantitative cell biology.
Mammalian Cell Signaling
A second goal in the Sorger Lab is to understand the pathways of mammalian cell signaling from a systems – rather than a component by component – perspective. Mathematical and experimental analysis of primary and transformed human cells are used to elucidate the biochemical circuits by which the insulin-like and epidermal growth factors, and the TRAIL and TNF death ligands, exert opposing effects on cell survival. The lab is particularly interested in elucidating differences between normal and diseased cells with respect to their responsiveness to anti-cancer drugs. Towards this end, we analyze large sets of protein-based data using both statistical and mechanistic mathematical modeling.