Synthetic and Systems Biology

We quantify gene-regulatory mechanisms at single-cell resolution in development, disease, and immunity, and build synthetic-biology tools for precise perturbation, control, and cellular design.

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Cell Systems 2026 paper image
Cell Systems 2026 · Transcriptional memory and cell states
PNAS 2026 paper image
PNAS 2026 · Deep learning and directed evolution
iMeta 2026 paper image
iMeta 2026 · Organoid-immune co-cultures
Nature Chemical Biology 2025 paper image
Nature Chemical Biology 2025 · Mammalian directed evolution
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Research Directions

Quantitative single-cell gene regulation and network dynamics

We integrate live-cell imaging, single-cell omics, and computational modeling to understand how TF activity, transcriptional bursting, epigenetic regulation, and regulatory networks generate dynamic gene expression in individual cells.

Cellular heterogeneity, state memory, and fate control

We study heterogeneity, heritable state memory, and fate transitions in stem, immune, and cancer cells, identifying regulators that explain and control functional differences among cells.

Pastomics: decoding cell history from single-cell snapshots

We combine single-cell and spatial multi-omics with algorithms to read how each cell arrived at its current state — which regulators were activated along the way and which programs persisted across generations — aiming to make lineage reconstruction without physical tracing routine.

AI-driven cell programming and synthetic biology

We use machine learning, deep learning, and high-throughput experiments to model complex biological systems and advance mammalian directed evolution, synthetic gene circuits, and predictable cell-control technologies.

Recent work

We combine quantitative understanding of living systems with new ways to regulate and design cellular function.