报告摘要:

The nonlinear interaction between different degrees of freedom in physical systems could produce diverse behavior, including chaos and turbulence. Hence, increasing number of agents with nonlinear dependence has the potential of generating extremely complex motion which defies most conventional computation techniques. In systems biology, the molecular interactions in cell regulation network show great nonlinearity and heterogeneity, and thus provide both opportunity and challenge for complex systems modeling. How to do efficient computation in these or similar complex systems is a research frontier faced by scientists from almost all branches. Here, I will present some  work that I have contributed to this effort and possible directions in my future exploration. Specifically,I will sketch a  recurrence pattern program and its application in the Kuramoto-Sivashinsky system. With cell regulation network, I emphasize the role of noise and its computation. Finally,  graph theoretic methods and dynamical systems analysis are will be used to decompose, analyze and simplify large-scale biological networks.