Dr. Xinguang Zhu, a postdoctoral researcher in the UIUC Department of Plant Science, will speak on the topic of "Biological Cellular Metabolism Simulation in the Post Genomic Era."

Presentation slides: [Powerpoint] [PDF]

Abstract:

Cellular metabolism simulations are essential for not only improving our basic understanding about metabolisms, and improving our ability to engineering better metabolisms for defined purpose. The constraint based modeling, data based modeling, and kinetic modelings are the three main techniques used to model metabolisms. Each of these three different techniques needs different data or parameter sets, uses different algorithm, and can provide information about different aspects of metabolism, and inevitably has shortcomings of its own.

Photosynthesis, inarguably one of the most important biological metabolism on earth, is a exceptional model metabolism system in that it is well studied, with many measurable signals probing different sections of this process. Yet at the same time, it provides many opportunities for system biology research, e.g. the system properties nearly not studied, the interaction of this process with other metabolisms in leaf not well characterized, and the target photosynthesis gene to manipulate for high crop yield not identified. There are many challenges ahead that need to be solved before we can accurately simulate photosynthesis performance under different time scale and different environmental conditions, and pinpoint targets for improving photosynthesis efficiency.

Biography:

Xinguang Zhu received his MS degree from Institute of Botany, Chinese Academy of Sciences in 1999. He received his Ph.D. from UIUC in 2004 with a thesis titled "Computational Approaches to Guiding Biotechnological Improvements in Crop Photosynthetic Efficiency." His main research interest is to accurately simulate the photosynthetic and associated metabolisms in natural environmental conditions at different time scales, study the system properties of the photosynthesis system, and explore ways to increase photosynthesis yield. He is especially interested in combining the large amount of genomic, microarray, proteomics, and metabolomics data with modeling techniques to improve understanding about photosynthesis.