科学研究

获美国华盛顿大学（西雅图）数量遗传学博士学位，曾任美国宾夕法尼亚州立大学统计学、公共卫生科学杰出教授，统计遗传研究中心主任。现任北京应用数学研究院研究员、清华大学丘成桐数学科学中心曾思明讲座教授。获国家自然科学基金会杰出青年基金（B类）、中组部。当选美国科学促进会（AAAS）会士、美国统计学会（ASA）会士。研究兴趣包括，发展跨学科统计方法，揭示复杂性状及人类复杂疾病的遗传控制机理。提出的基因功能定位（functional mapping）方法能有效发现性状发育的遗传规律，刻画基因效应随时空变化的关键模式。将功能定位理论与进化博弈论、尺度理论、捕食者-食饵理论、发育模块理论相结合，提出解析复杂性状的功能图论（functional graph theory），并据此发展出一系列计算方法用于构建从分子到表型的多层次、多空间、多刻度的基因型-表型关系立体几何网络，为系统生物学、系统医学研究提供分析工具。在Nature Reviews Genetics、Nature Communications、PNAS、Journal of the American Statistical A

Natural phenomena are inherently complex, which can be viewed as dynamic systems in which constituent elements act and interact across time and space scales in a complicated but coordinated manner. Disentangling the detailed architecture of intercomponent interactions and their role in mediating system behavior can reveal the general rules behind natural phenomena and processes. In this talk, I will present an emerging theory – functional graph theory (FunGraph) derived from the combination of evolutionary game theory, predator-prey theory and developmental modularity theory into graph theory. FunGraph can chart the mechanistic roadmap of how each element influences system dynamics directly or through a web of indirect pathways. We demonstrate the use of FunGraph to reveal the internal workings of the gut microbiota associated with human health. We discuss the advantages and disadvantages of FunGraph as a statistical mechanics tool to unveil the operational mechanisms of general complex systems.