报告题目：Causal gene networks in single cells
报告摘要：Causal networks encode the driving forces of complex systems and are notoriously hard to reverse engineer. In biomedicine, causal gene regulatory network (GRN) is a fundamental determinant of cell differentiation and behavior and ultimately organismal (dys)function. The inference and understanding of causal gene regulations and their phenotypic outcomes arguably have become the most studied question in biomedicine. Recent single-cell multi-omic technologies allow to reconstruct causal GRNs at a fraction of cost with unprecedented statistical accuracy and cell-type specificity. In this talk, I will start from causal GRN inference with bulk genome-transcriptome variations. I will then transfer this causal inference approach to single-cell CRISPR screens to scan for causal gene regulations efficiently at scale. Finally, I will combine single-cell transcriptome and chromatin accessibility profiles to unveil the dynamic rewiring of direct causal GRN throughout cell differentiation. These studies delve into the nature of causal systems and seek practical biomedical insights.
报告人简介：王凌飞博士现为University of Massachusetts Medical School助理教授。他曾在英国兰卡斯特大学获得理论物理学博士，研究早期宇宙暴涨理论，并于爱丁堡大学、美国Broad Institute、哈佛大学医学院完成博士后研究，进行基因调控网络的重建与分析。王凌飞博士的主要研究兴趣为开发新的计算生物学方法，由数据重建基因调控网络，尤其在单细胞和时空多组学领域，基于因果推断、统计推断、机器学习、算法设计等开展基因网络、遗传学、单细胞及时空组学、发育生物学等领域研究有着深入研究。王凌飞博士在Science、Nature Methods、Nature Communications等期刊发表多篇文章。
报告时间：2023年11月30日 下午2:00
报告地点：吉林大学人工智能学院（正新楼）三楼报告厅
