The immune system has a remarkable ability to distinguish between healthy (self) and unhealthy (non/altered self) cells and eliminate only the latter. This process can depend on a single cell or millions of cells that transmit information to one another.

OUR GOAL: Inspired by the way the immune system works, we aim to decode, rewire, and develop new and effective mechanisms to selectively eliminate and reprogram “altered self” disease-causing cells (as cancer, senescent, virally infected, and other dysfunctional cells) for disease treatment and prevention.

OUR APPROACH: Our work is at the interface between cell engineering, synthetic biology, functional genomics, machine learning, and immunology. We integrate in-silico/ex-vivo/in-vivo systems to probe, track, and redirect complex biological processes across scales. Using genetic perturbations with high-content, high-throughput readouts, AI/data-driven experimental design, and directed evolution we perform multiplexed experiments and scan large combinatorial search spaces to (1) rapidly optimize different cell functions of interest, (2) identify non-linear interactions to obtain precise and targeted interventions with desired context-specific effects, and (3) decode and integrate mechanisms that have evolved for millions of years with mechanisms that we “evolve” and rationally design in the lab.

OUR DISCOVERIES: We recently identified programmable mechanisms to engineer T cells and Natural Killer (NK) to redirect these lymphocytes into solid tumors (providing a basis for spatially targeted cell therapies), identified RNA-based interventions that selectively sensitize cancer and virally infected cells to immune-based elimination (selectively eliminating these harmful cells without harming healthy ones), developed new technologies to track the impact of genetic perturbations in the intact tissue, and mapped tumor organization at unprecedented scales.