Our goal is to advance predictive biology for tissues. We seek to uncover fundamental principles by which cells interact to generate stable, adaptive, and resilient tissues in the face of fluctuating environments. We view tissues not as collections of cell types, but as dynamic circuits composed of interacting cells that together govern homeostasis, remodeling, and failure. Our central question is: what are the minimal cell-cell circuit motifs that sustain tissue function? How are these circuits rewired across development, physiological fluctuations, aging, and disease? Drawing inspiration from systems theory, we aim to define the “grammar” of cellular interactions that underlie robust tissue behavior.

We study these principles across the ovarian and cardiovascular systems to identify conserved and context-specific rules of tissue organization. By integrating cutting-edge spatially resolved and genomics technologies, machine learning, and mechanistic models, we investigate how cells encode dynamic organismal cues such as hormonal cycling, inflammation, and stress, and how cell-intrinsic and cell-extrinsic interactions self-organize into reproducible spatial patterns during tissue remodeling. Ultimately, our goal is to move beyond descriptive atlases toward causal, predictive models of tissue organization, enabling the discovery of mechanisms that drive resilience versus failure to inform new therapeutic strategies for age-related and hormone-dependent diseases.