北京大学定量生物学中心
学术报告
题目：Understanding the development, differentiation and diseases of intestinal stem cells
报告人：Tongli Zhang, Assistant Professor
Department of Molecular & Cellular Physiology College of Medicine, University of Cincinnati
时 间：2016-6-6（周一），13:00-14:00
地 点：北京大学老化学楼东配楼一层101报告厅
主持人：魏平 研究员
摘 要:
Being developed from pluripotent embryo stem cells, multipotent intestinal stem cells continuously differentiate into mature functional cells (e.g. Enterocytes, Goblet cells, Enteroendocrine cells and Paneth cells). This essential process is tightly regulated in healthy organisms and its deregulation leads to diseases. As current molecular and cellular biology reveals the regulatory details of the pathways (e.g. Wnt, Notch, MAPK, BMP) controlling this process, the accumulated information proposes the challenge of integrating these details together into a logical and dynamical framework. In order to cope with this challenge, we have converted several key pathways into computational models. These model have been constrained with available data reported in the literature, and the novel predictions generated by these models are being tested in intestinal enteroids, intestinal organoids as well as animals.
报告人简介：
Tongli zhang was educated at China Medical University, where he studied clinical medicine and experimental biology. Later, he received his PhD in Genetics, Bioinformatics and Computational Biology (GBCB) under John Tyson’s mentorship at Virginia Tech, and then took up a postdoc with Bela Novak at the University of Oxford. Now he is an assistant professor in the Department of Molecular & Cellular Physiology College of Medicine at University of Cincinnati.As a computational cell biologist, Dr. Tongli Zhang constructs mechanistic models to connect cellular behaviors and molecular control mechanisms. In collaboration with molecular cell biologists, he refines these models by careful comparison to published and novel experimental data. He believes that iterative cycles between modeling and experiment will lead to mature models, which will help the biomedical community to understand and treat complex diseases.
