The Cahan Lab is a hybrid computational/experimental group that invents computational tools that distill omics data down to specific, testable hypotheses in the contexts of stem cell biology, developmental biology, and cell engineering. Most of our computational efforts are ‘single cell’ or spatial in nature, and thus this central part of our research fits with the ‘Single-cell/Spatial Transcriptomics’ theme of TTEC. Examples of computational platforms that we have created are 1. machine learning tools that measure the extent to which engineered cell populations reflect their natural counterparts, and 2. algorithms that predict the impact of cellular perturbations on cell engineered fidelity. Both of these applications can help to create and evaluate iPSC-derived disease models, which is another TTEC theme. Finally, we use these and other tools to uncover how cell lineages of the synovial joint emerge during development with the long term goal of leveraging this knowledge to engineer cells for regenerative medicine. This long-term goal aligns well with TTEC’s Tissue Engineering & Biomaterials Pillar and Healthy Aging theme.
