报告摘要

Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molecular mechanisms underlying cellular function. Despite significant advancements, challenges persist in GRN inference, particularly in dynamic rewiring, inferring causality, and context specificity. To tackle these issues, we present SiCNet, a novel causal network construction method that utilizes single-cell gene expression profiles and a causal inference strategy to construct molecular regulatory networks at a single cell level. Additionally, SiCNet utilizes cell-specific network information to construct network outdegree matrix (ODM), enhancing the performance of cell clustering. It also enables the construction of context-specific GRNs to identify key regulators of fate transitions for diverse processes such as cellular reprogramming and development. Furthermore, SiCNet can delineate the intricate dynamic regulatory processes involved in development, providing deep insights into the mechanisms governing cellular transitions and the gene regulation across developmental stages.

个人简介

刘小平，国科大杭高院生命与健康科学学院研究员，2012年毕业于上海大学信息学与系统生物学专业，日本东京大学博士后，2017-2020年山东大学数学与统计学院研究员。主要研究领域是：单样本网络构建与分析、因果推断方法、多组学整合分析等。在National Science Review、Nucleic Acids Research、Briefings in Bioinformatics、Cancer Letters、Bioinformatics、PLoS Computational Biology等杂志发表SCI论文50余篇。现为中国医药生物技术协会基因检测分会委员，中国运筹学会计算系统生物学分会会员，生化细胞协会分子系统生物学专委会委员。SCI杂志Mathematics编辑,SCI杂志Frontiers in Genetics和Symmetry-Basel的客座编辑。

报告时间：2025年5月17日8:00-12:00

报告地点：北衡楼1421