Systems biology utilizes both experimental and computational approaches to study biological networks at a systems- or network-level, in order to understand and predict cellular behavior. Our group studies metabolism and regulation by combining computational and experimental approaches. Overall, we use computational models to study biological systems, engineer cells, and expand our knowledge of the underlying mechanisms behind observed cellular behaviors. We are interested in studying microbes (and microbial interactions) involved in metabolic engineering, bioremediation and health applications. Our models enable the integration of diverse sets of experimental data to predict the structure and activity of cellular networks. With regard to structure, we are interested in identifying novel enzymes and/or reactions, transcriptional regulatory interactions, and inter-species interactions to further elucidate genotype-phenotype relationships. We are also interested in quantifying network activities using a variety of experimental (e.g., 13C metabolic flux analysis) and computational tools (e.g., constraint-based and kinetic models). Developed models allow us to systematically evaluate the capabilities of different organisms from a network-based perspective and to identify ways in which genetic manipulations could enhance desired activities, such as chemical production.