报告题目：Towards a Quantitative Understanding of Cellular Dynamics via Lineage Tracing Inference

报告人：周达 教授

报告时间：2026年6月1日下午4:00

报告地点：数统学院402

邀请人：刘勇进

邀请单位：福州大学数学与统计学院

报告摘要：Understanding how cells make fate decisions is central to developmental biology, tissue homeostasis, and disease progression. Lineage tracing data record this information, but in practice they are typically available only at the terminal cells, making it challenging to infer the underlying cellular trajectories. Stochastic modeling provides a principled approach to capture the inherent randomness of cell state transitions and to leverage partial lineage information.We develop a series of lineage-guided stochastic models for lineage-traced single-cell data. First, PhyloVelo models gene expression as a stochastic differential equation along cell lineages to estimate transcriptional velocities and reconstruct differentiation trajectories. Second, scPhyloX infers stem and non-stem cell dynamics during tissue growth using only phylogenetic trees, demonstrating the power of stochastic modeling even without transcriptomic data. Third, we investigate the utility of mitochondrial DNA mutations for lineage tracing, highlighting the advantages and limitations of clonal inference from mtDNA variants. Overall, our work demonstrates the value of lineage-guided stochastic modeling in quantitatively decoding cell fate decisions from single-cell observations. These methods advance our understanding of cellular heterogeneity, differentiation, and developmental dynamics, with potential applications in both normal tissue and cancer biology.

报告人简介：周达，厦门大学数学科学学院教授，博士生导师。2006年于华中科技大学数学系取得统计学专业学士学位，2011年于北京大学数学科学学院取得概率统计专业博士学位；2011-13年在清华信息国家实验室从事博士后研究工作。长期从事概率统计与生物医学、化学等领域的交叉研究，共发表论文近60篇，其中以第一或通讯作者论文发表在Nature子刊、Cell子刊、SIAM系列等知名刊物；先后主持国家自然科学基金3项，企业横向课题5项；成果荣获2023年中国生物信息学“十大进展”；荣获2021年厦门大学田昭武交叉学科奖；目前兼任厦门大学数学科学学院副院长、厦门大学健康医疗大数据国家研究院副院长。