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  • 统计与管理学院2018年学术报告第26期

    【主 题】 Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level

    【报告人】 Min Chen  副教授

    University of Texas at Dallas

    【时 间】 2018年06月25日(星期一)16:00-17:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】R Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.

    嘉宾简介】Dr. Chen received his B.S. degree in Computer Science from University of Science and Technology of China in 1994, and received his PhD degree in Decision Science and Statistics from University of Texas at Austin in 2006.  He did postdoc research on Statistical Genomics in Yale University from 2008 to 2010.  Previously he was an assistant  professor of Biostatistics in Univeristy of Texas Medical Center in Dallas.  Currently he is an associate professor of Statistics in University of Texas at Dallas. His research focuses on statistical modeling and analysis of clinical studies, high-dimensional and large-scale genomic data.  He has published many statistical methodology papers and collaborative research papers. His research has been supported by multiple federal and state grants.