| The metabolic network of a cell consists of hundred to thousands of small molecule species intricately linked by an even larger set of biochemical reactions. The expansive and highly connected nature of this important cellular system greatly limits the degree of insight that may be gained from the isolated study of a single component or module. Instead, true understanding of the function, fragility, and evolvability of the metabolic network requires a systems level approach that relates properties of individual components to the large-scale behaviors that emerge from their interaction. Such an approach must combine high-throughput global measurements of cellular physiology with a mathematical modeling framework for the interpretation of experiments and formulation of hypotheses regarding cellular function.; In this dissertation, I introduce and apply a number of techniques for systems analysis of genome-scale metabolic networks. In the first study, I combine an in silico analysis of E. coli production capabilities with a high throughput in vivo knockout dataset to suggest essential and non-essential components of the E. coli metabolome. In the second study I introduce a novel principle regarding the conservation laws of metabolic networks, which I then apply to identify, hundreds of novel minimal nutrient sets that support in silico growth of E. coli. In the third study, I introduce a technique for the characterization of lethal mutants that induce toxic byproduct coupling between species in a, metabolic network. In the final study, I outline a systematic approach for computing minimal reaction knockouts that disable a given metabolic function. I apply this analysis to the genome-scale metabolic networks of E. coli., S. cereviseae, and H. influenzae to generate hundreds of complex deletion mutants that suppress in silico growth in rich media. |
