应云顶国际4008服务平台的邀请，加拿大 New Brunswick大学副教授Lin Wang博士将于4月26日来我校进行学术交流并作学术报告。
时 间：4月26日(星期五)上午10：00
地 点: 理科楼202会议室
报告题目：An immunosuppressive infection model: mathematical theory and biological implications
报 告 人：Lin Wang, University of New Brunswick, Canada
摘要：Sustained and transient oscillations are frequently observed in clinical data for immune responses in viral infections such as human immunodeficiency virus, hepatitis B virus, and hepatitis C virus. To account for these oscillations, we incorporate the time lag needed for the expansion of immune cells into an immunosuppressive infection model. We show that the delayed antiviral immune response can induce sustained periodic oscillations,transient oscillatios and even sustained aperiodic oscillations (chaos). Both local and global Hopf bifurcation theorems are applied to show the existence of periodic solutions, which are illustrated by bifurcation diagrams and numerical simulations. Two types of bistability are shown to be possible: (i) a stable equilibrium can coexist with another stable equilibrium, and (ii) a stable equilibrium can coexist with a stable periodic solution. This bistability pattern allows us to further explore the joint effects of the duration of therapy, the efficacy of the drugs and the time lag in immune expansion on immunity boosting for a single phase of therapy. Our results provide some insights on how to successfully establish sustained immunity by carefully designed antiviral therapy. In addition, several multi-phase therapy strategies are compared and the factors that cause the failure of launching sustained immunity are also discussed.
报告人简介： Dr Wang got his Ph D in Memorial University of Newfoundland in 2003. Now he is associate professor in the department of mathematics and statistics, University of New Brunswick. His research interests are in di_erential equations and applied dynamical systems with applications in biology, bioengineering, ecology and neuroscience. Primarily, I work on: (i) mathematical modeling of infectious diseases; (ii) gene and neural networks; (iii) chemostat
models and (iv) population dynamics.
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