报告题目：Network biomarker for quantifying regular state of biological systems, and dynamic network biomarker for quantifying critical state of biological systems
报告人：陈洛南 研究员
主持人：周 琦 教授
时 间：2021年6月15日（周二）下午4点
地 点：纳米楼457报告厅
报告人简介：
Luonan Chen received BS degree in the Electrical Engineering, from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology, Shanghai University. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. In recent years, he published over 350 journal papers and two monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning.
Abstract:
We defined two new types of biomarkers to quantify the states of biological systems based on network, in contrast to the traditional molecular biomarkers. Network biomarker is constructed to quantify regular state of a biological system, while dynamic network biomarker is to quantify the critical state or tipping point of a biological system. (1) Network biomarker (NB) is a subnetwork or network module, which is composed of a number of associations or regulations between molecules (or variables), rather than simply a number of molecules. Those associations (the second-order statistics) in the module are formed collectively as a biomarker, thus robustly and accurately quantifying the regular state of a biological system, completely different from the concentrations of conventional molecular biomarkers (the first-order statistics). (2) Dynamic network biomarker (DNB) is a subnetwork or module, and is also composed of a number of associations or regulations between molecules but with three statistical conditions (in terms of variances and covariances), which are actually a number of strongly and collectively fluctuated molecules in the network. Theoretically, DNB is able to quantify the critical state or the tipping point of a biological system, thereby serving as a general early-warning signal to indicate an imminent state transition. A number of real datasets are provided to validate the effectiveness of NB and DNB.
