| The central theme of this thesis is the development of computational tools for the analysis and redesign of metabolic networks. The key questions that are addressed in this work are: how can one (1) select the optimal set(s) of functions to recombine into microbial strains to enhance their maximum theoretical production capabilities, (2) identify the smallest set of reactions capable of supporting minimal metabolism under various conditions, (3) test whether hypothesized intracellular objectives are consistent with experimental measurements, (4) shape the connectivity of a metabolic network in such a way that a targeted biochemical becomes an obligatory byproduct of cell growth, and (5) elucidate the topological and structural features of metabolic reconstructions at the genome-scale.; In the first part of this thesis, a computational protocol is introduced and applied to select the optimal foreign reactions from a genomic database encompassing multiple species for increasing the maximum theoretical yields of the twenty amino acids in Escherichia coli. Here it was found that the production capabilities of seven amino acids could be enhanced by the addition of only one or two foreign functionalities. Next, the minimal reaction network framework revealed that 224 or 229 metabolic reactions are required to support E. coli growth on glucose or acetate, respectively, while a cell cultured on a rich optimally engineered medium could theoretically support growth with as few as 122 metabolic reactions. Thus the minimal reaction sets were revealed to be highly dependent on the imposed uptake environment and the growth requirements. In the following section, the ObjFind procedure is applied to two sets of experimental E. coli flux data (i.e., anaerobic and aerobic growth) revealing that a common metabolic objective, biomass production, is the most explanatory. The OptKnock framework is then introduced for pinpointing reactions in a metabolic network for removal to force growth-coupled biochemical production. Computational results for the overproduction of lactic acid, succinic acid, and 1,3-propanediol are consistent with both intuitive and non-intuitive knockout strategies published in the literature. Lastly, the flux coupling finder procedure is applied to three genome-scale metabolic models enabling the exhaustive identification of various types of reaction coupling. |
