UC San Diego

Characterization of complex cellular behaviors on a molecular scale requires detailed understanding of the components and properties of the biological system. With the availability of genome sequences and high-throughput data, the mathematical and computational modeling of biological system has made tremendous advances in describing system-level behaviors and properties. For example, the dynamic analysis on kinetic models of metabolism has contributed to the understanding of temporal hierarchy of dynamic events, as well as the elucidation of fundamental dynamic structures of the network. Thermodynamic analysis on the biochemical reaction networks has revealed the fundamental constraints governing various cellular processes and interactions. Additionally, constraint-based analysis in the context of genome-scale models of metabolism and gene expression has been used to compute the optimal metabolic flux state and proteome allocation for the given phenotype. In this dissertation, I am interested in applying multi-scale modeling to characterize the biological network structure and components. First, the dynamic network structures and properties at different timescales are explained through kinetic modeling of metabolism. Next, the evolutionary tradeoffs due to thermodynamic favorability and pathway yield in biosynthetic pathway choice of different organisms are revealed through the combination of thermodynamic analysis and genome-scale metabolic models. Last, the adaptive response of E. coli under acid stress is examined through laboratory evolution and such response is characterized through a mechanistic approach using genome-scale model of metabolism and gene expression.