Research
Our goal is to make tissue biology predictive. Understanding how tissues age requires measuring, modeling, and ultimately predicting cellular behaviors in complex environments. By combining machine learning, spatial genomics, and perturbation experiments, we aim to build predictive models that uncover the principles governing tissue resilience.
How do tissues lose resilience with age? Aging is a systems-level failure of tissue organization. We view tissues as dynamical systems. Our inspiration draws upon systems theory and dynamical systems.
Core questions:
Can we identify the rules that govern how cells self-organize into functional tissues?
How do cellular interactions generate stable, adaptive, and resilient tissues in the face of fluctuating environments?
Current Themes
Predictive Tissue Biology
How do tissues maintain stable function despite continual cellular turnover, extracellular matrix remodeling, environmental perturbations, and aging? We combine spatial genomics, histopathology, machine learning, and dynamical systems modeling to identify the multicellular circuits that govern tissue behavior. Our goal is to define stable tissue states, understand the transitions between them, and build predictive models that explain how tissues adapt, remodel, and ultimately fail in disease.
Aging and Tissue Resilience
The ovary is one of the first organs to age, yet the mechanisms that drive its decline remain poorly understood. Both its reproductive and endocrine functions depend on cyclical changes in cellular composition, molecular programs, extracellular matrix remodeling, and multicellular interactions that repeat throughout the reproductive lifespan. We combine spatial profiling, lineage tracing, and machine learning to uncover how these dynamic processes become disrupted with age, with a particular focus on immune, vascular, and stromal dysfunction. By studying the ovary as a model system, we aim to uncover general principles of tissue resilience, translate these insights to human biology, and identify mechanisms that preserve function across the lifespan.
We are grateful to our funding sources for their support:
