Efficient Analysis of Dynamical Properties in Stochastic Chemical Kinetic Models PDF slides
Hiroyuki Kuwahara, SCS, Carnegie Mellon
04/02/2010, 2PM, GHC-6501
Biological processes are inherently stochastic at the molecular level. Yet, it is such random events that remarkable features of living organisms stem from. In other words, through a long process of evolution, living organisms have acquired mechanisms to tightly control such underlying uncertainties so as to increase their fitness in their living environments in a variety of ways. In order to study how biological systems control and exploit stochastic effects in silico, stochastic simulation has become an important tool. However, as we are beginning to address more complex and sophisticated biological questions than ever before, it has become increasingly clear that no single modeling and simulation method can satisfy the needs of a wide array of important biological questions. This, in turn, makes it essential to develop modeling and simulation approaches to tailor for given system properties of interest. In this talk, I will present several such methods to balance the accuracy and the efficiency of computational analysis for specific problems of interest by using enzymatic cycles---a ubiquitously found biological control motif---as a running example.
Hiroyuki Kuwahara obtained his B.S. and Ph.D. in computer science from the University of Utah. His Ph.D. thesis describes systematic and automatic model abstraction methodology to efficiently estimate temporal behaviors of genetic regulatory networks. To further pursue his research in the multidisciplinary field of computational biology, He worked for Microsoft Research – University of Trento Centre for Computational and Systems Biology. He is a Lane fellow in the Ray and Stephanie Lane Center for Computational Biology at Carnegie Mellon University.