Abstract

The traditional view suggests that aging is a progressive decline of physiological and physical function with passage of time. However, increasing evidence shows that aging may be subject to abrupt change in metabolic capacity within a certain time window of the lifespan. This phenomenon, in conjunction with the multifactorial etiology of aging, makes it extremely difficult to portray a comprehensive atlas of when and how aging is progressed. In this talk, I will present a new norm of statistical mechanics for coalescing all aging-related or even -unrelated factors into informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from cross-sectional data. The integration of GLMY homology theory into idopNetworks hastens the formulation of statistical topology as a new theory to extract and excavate the fundamental principles behind aging processes from increasing bulks of data at various levels of organization from molecules to cells to organs to total organisms. I will demonstrate the applied value of the statistical topology in disentangling aging in practice.

Speaker Info

Rongling Wu, received a Ph.D. in Quantitative Genetics from the University of Washington (Seattle) in 1995. He was a Distinguished Professor of Statistics and Public Health Sciences at Pennsylvania State University, and Director of the Center for Statistical Genetics. He is currently the Zeng Siming Chair Professor of Yau Mathematical Sciences Center, Tsinghua University. He is also a researcher at Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and also serves as editor-in-chief, associate editor, special editor and editorial board member of several journals in the fields of genetics, bioinformatics and computational biology. He was selected as a fellow of the American Association for the Advancement of Science and the American Statistical Association, and won the Distinguished Researcher Award of the American Institute of Applied Mathematics and Statistics (SAMSI), the University of Florida Research Fund Professor Award, the Pennsylvania State University Distinguished University Professor Award, and the Floyd Science Innovation Award. Research interests include: developing interdisciplinary statistical methods to reveal the genetic control mechanisms of complex traits and human complex diseases. The proposed functional mapping method can effectively discover the genetic rules of trait development and describe the key patterns of gene effects changing over time and space. Combining functional mapping with evolutionary game theory, scale theory, and prey-predator theory, a series of computational methods have been developed to construct multi-level, multi-space, and multi-scale genotype-phenotype relationships from molecules to phenotypes The three-dimensional network provides analysis tools for systems biology, systems medicine, and systems pharmacology research. Published more than 400 SCI papers in important international journals such as Nature Reviews Genetics, Nature Communications, PNAS, Journal of the American Statistical Association, Annals of Applied Statistics, Physics of Life Reviews, Physics Reports, Briefings in Bioinformatics, Cell Reports, Evolution, etc. The research results have been cited or highlighted by important journals such as Science and Cell.