Measuring and Modeling Aging from Cells to Tissues

Eric Sun (Harvard and MIT) Aging is an incredibly complex biological process that spans different molecular and cellular processes and traverses multiple biological scales. With the advent of technological platforms for high-dimensional profiling of biological systems, such as spatial and single-cell omics, there is now a deluge of data to characterize complex processes like aging. Here, we propose several machine learning and computational frameworks that leverage large-scale spatial and single-cell gene expression data to model and track aging at the level of individual cells. We further present deep learning models that extrapolate changes in single cells to their spatial effects on the surrounding tissue, bridging multiple scales of organization and enabling computational generation of new hypotheses. Eric is an Assistant Professor of Biological Engineering at MIT and a Member of the Ragon Institute of Mass General Brigham, MIT, and Harvard, where he leads an independent research program that focuses on developing computational and AI/ML approaches to decode and control the biology of aging. Previously, Eric completed his Ph.D. in Biomedical Informatics at Stanford University and obtained an A.B. in Chemistry and Physics and S.M. in Applied Mathematics from Harvard University.