报告题目：The dynamical system analysis of single-cell omics data

报 告 人：周沛劼研究员（北京大学）

报告时间：2024年6月18日  9:30

报告地点：格物楼数学研究中心528

报告摘要：

Single-cell sequencing technologies provide unprecedented resolution for studying the dynamic process of cell-state transitions during development and complex disease. In this talk, I will discuss how machine learning has enabled us to overcome this challenge and use dynamical systems techniques to analyze scRNA-seq data. I will introduce the low-dimensional dynamical manifold to identify attractor basins and transition probabilities in snapshot data. I will also present the usage of non-equilibrium dynamical systems theory to analyze attractor stability and identify transition-driving genes in gene expression and splicing processes. Finally, I will discuss our efforts to construct a time-varying landscape, which interpolates non-stationary time-series scRNA-seg data using Wasserstein-Fisher-Rao metric, unbalanced optimal transport and its neural network-based partial differential equation implementations.

报告人简介：

周沛劼，北京大学前沿交叉学科研究院国际机器学习中心研究员，博士生导师，助理教授。入选国家海外高层次青年人才计划。2019年毕业于北京大学数学科学学院，2020-2023年担任美国加州大学尔湾分校访问助理教授。研究领域为计算系统生物学。主要科研兴趣为数据驱动的动力学建模与计算，研究成果发表在Nature Methods，Nature Communications，Physical Review X，Molecular Systems Biology等交叉学科期刊。曾于2019年获得北京大学优秀博士论文奖。