Please join the Department of Chemical Engineering on Thursday, February 14, for a guest lecture by Dr. Zhixing Cao (Edward), from the University of Edinburgh, speaking on Linear Mapping Approximation of Nonlinear Gene Regulatory Networks with Stochastic Dynamics.
The presence of protein-DNA binding reactions often leads to analytically intractable models of stochastic gene expression. In this talk, Dr. Zhixing Cao will present the linear-mapping approximation that maps systems with protein-promoter interactions (nonlinear networks) onto approximately equivalent systems with no binding reactions (linear networks). This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method's accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The analytic or semi-analytic solutions result in superior computation efficiency, hence enabling the possibilities of developing superior inference methods to facilitate interpretation of experimental data.
Dr. Zhixing Cao (Edward) received his B.Eng. from the Department of Control Science and Engineering, Zhejiang University, China, in 2012 and his Ph.D. degree in Chemical and Biomolecular Engineering, from the Hong Kong University of Science and Technology (HKUST) in 2016. He is now a postdoctoral fellow in School of Biological Sciences, the University of Edinburgh. Prior to that, he worked as a postdoctoral fellow in John A. Paulson School of Engineering and Applied Sciences, Harvard University.
He is also a recipient of the Hong Kong PhD Fellowship, Chan & Wong Best Graduate Award and several outstanding reviewer awards from top process system engineering journals. His research interests include batch process control, system identification and biological system modelling.
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