We focused on understanding design principles underlying the ability of cells to form spatial patterns, or “polarize”. Highlights of our past research program include the discovery of a fundamentally new polarity mechanism, referred to as the "neutral drift model," in which positive feedback drives recurrent stochastic clustering of signaling molecules, a new approach for reverse-engineering information flow among signaling modules in rapidly polarizing human neutrophils, and an investigation into the developmental rules of the neural superposition brain circuit in the fly visual system.
Our lab pioneered computer vision and machine learning approaches for scalable, microscopy-based drug discovery. Over the past decade, we advanced approaches for identifying optimal biomarkers, extracting informative signatures of cellular perturbations and quantifying stereotyped cellular responses. Our methodology has been widely adapted in academics and industry. Quantitative microscopy is at the heart of many ongoing research projects in the lab.
Our lab pioneered experimental, computational and theoretical approaches for identifying functional roles in patterns of cell-to-cell differences. We showed that patterns of cell-cell differences can reveal hidden biological information about how cells make decisions and serve as an informative readout of disease progression and drug response. Understanding the origins and impact of cellular heterogeneity has profound implications to all fields of biology, from understanding emergent behaviors of living systems, to developing improved approaches for tissue engineering, to treating disease.