I come to biology because I love to solve problems. From very early on I realized that, to become a good problem solver, one needs to make friends with master problem solvers, or to gather ideas from finely crafted solutions to hard problems. Where else can one find a countless number of good problem solvers than in biology? From the folding of a protein into a distinguished shape to the efficient usage of nutrients in biosynthesis of biomass and onto the hunting strategies of a pack of wolves, living organisms are loaded with amazing solutions to earn their place in today's biosphere. But the researcher needs to live and play in that world to truly appreciate the beauty and ingenuity in the solutions. The exercise often requires examining the process in quantitative detail. This type of research is commonplace in physics but has become possible in biology (on a large scale) only recently due to the rapid development of modern observational tools such as fluorescent microscopy and mass spectroscopy. The dazzling array of genomic sequencing and genetic manipulation techniques also open up tremendously new possibilities to probe and interfere with a living system in novel ways. Here at the QSBC, we are developing in house experimental capabilities and modeling and computational platforms that allow participating groups to carry out quantitative study of cellular processes under defined conditions.
My current research focuses on the elucidation of regulatory interactions in cellular metabolism, particularly for bacteria. Previous modeling work, particularly those carried out by Palsson's group at UCSD, have led to the predictions of optimal growth rates based on a mathematical technique known as linear programming. Yet a growing cell relies on a large number of feedback control and homeostasis of key currency compounds to manage the metabolic traffic. Given the extremely complex nature of the problem, we have taken the following strategy in our study: i) Analysis of network topology and flux partitioning by following the carbon flow; ii) Identification of regulatory interactions with effective degrees of freedom in network traffic; iii) Study of flux control through linear pathways using methods of nonlinear dynamics and phase space analysis and iv) Step-by-step and hierarchical systems level integration based on coarse-grained description of individual pathways. Our long-term aim is to develop a modeling framework to integrate protein abundance, enzyme activity, and flux measurements for realistic dynamic simulation of metabolic flow under various growth conditions. An active collaboration with Z.W. Cai's group in Chemistry and J.H. Zhang's group in Biology on the interaction between pyrimidine and arginine synthesis pathways in E. coli is under way.
Aside from the projects described above, there is also ongoing work in the group on i) Population level modeling of bacteria growth and migration inside a tumor and its effect on tumor growth (in collaboration with J.D. Huang at Biochemistry, HKU); ii) Experimental and theoretical study of osmostress response through the Hog pathway in yeast (in collaboration with J. H. Zhang at Biology); iii) Statistical mechanical study of noise propagation in molecular networks (in collaboration with L.P. Xiong and Y.Q. Ma at Nanjing U); iv) Bioinformatics study of synergistic gene regulation and regulatory hierarchy (i.e. the role of global and specific transcription factors) in yeast.