北京大学定量生物学中心
学术报告
题 目: Modeling and Navigating Cell State Transitions with Deep Learning
报告人: Pan Deng, Ph.D.
Associate professor at Beijing Zhongguancun Academy, China.
时 间: 6月22日（周一）13:00-14:00
地 点: 吕志和楼B101
主持人: 周沛劼
摘 要:
Cell fate decisions define development, regeneration, and disease. Yet we still lack a unified framework to systematically map, predict, and control transitions between cellular states. Building a computational system that can both model the global cell state landscape and rationally steer cells from one state to another represents one of the central challenges of modern biology.
Leveraging advances in single-cell omics, deep learning, and the emerging concept of the virtual cell, we developed computational frameworks for modeling and manipulating cell states. We introduce CellNavi, which learns the cell state manifold and identifies genes driving state transitions, and GEMGen, a large language model–based framework for phenotype-oriented small molecule generation. Together, these methods uncover molecular determinants of state transitions during development and disease progression, and enable rational generation of compounds capable of inducing desired cell state changes. This work moves toward a programmable framework for understanding and steering cellular dynamics.
报告人简介：
Dr. Pan Deng is an associate professor at Beijing Zhongguancun Academy, China. She graduated from the School of Life Sciences at Tsinghua University in 2012 and earned Ph.D. in Cell and Molecular Biology from Cornell University and Memorial Sloan Kettering Cancer Center in 2018. Dr. Deng previously served as a Senior Researcher at Microsoft Research. Long dedicated to the intersection of artificial intelligence and life sciences, her research focuses on AI-driven systems biology and synthetic biology. She is committed to deciphering the fundamental laws of biological systems through computational modeling and advancing AI-based drug discovery. As first or corresponding author, her research has been published in top-tier journals, including Nature Cell Biology, Nature Machine Intelligence, Nature Communications, PNAS, and etc., accumulating over 1,000 total citations.
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