Systems metabolic engineering has enabled systematic studying of microbes for modifying their genetic contents, analyzing their metabolism, and designing new capabilities. One of the most commonly used approaches in systems metabolic engineering involves genome-scale metabolic flux modeling. These models allow generation of predictions of the global metabolic flux distribution in the metabolic network of organisms, in silico. With the current advances in genome sequencing technologies and the global demand for bio-based commodity chemicals and fuels, genome-scale models can help metabolic engineers propose design strategies while considering holistic behavior of the organism. In this research, novel tools and methodologies were developed to improve the future prospective of systems metabolic engineering with genome-scale modeling. To do this, an online web application (Synthetic Metabolic Pathway Builder and Genome-Scale Model Database, SyM- GEM) was first developed enabling the construction of synthetic metabolic pathway(s) and addition of those to synchronized genome-scale models. This addresses the need for an easy and universal way of creating models of engineered microbes with improved properties without the time-consuming inconvenience of synchronizing different formats and representations of genome- scale models prepared by different laboratories. The web application is freely available at http:www.mesb.bse.vt.edu/SyM-GEM. Then, a computational framework (Total Membrane Influx-Flux Balance Analysis, ToMI-FBA) was developed to allow for evaluating synthetic pathway use by different models. This enabled, for the first time, a computational guide for optimal host selection (for a specific metabolic engineering problem) and culture media formulation design to achieve the solution. Results showed that (i) L-valine improves isobutanol production by Bacillus subtilis, (ii) cellobiose increases ethanol selectivity by Clostridium acetobutylicum ATCC 824, and (iii) B. subtilis is an optimal host for artimisinate production. To further expand the capability of genome-scale models, an algorithm was developed (Genetic Algorithm-Flux Balance Analysis minimizing Total Unconstrained eXchange Flux, GA-FBA minimizing TUX) to help improve the fitness between metabolic fluxes predicted by genome-scale modeling and those obtained by 13C-tracing methods. Application of this method to the cyanobacterium Synechocystis PCC 6803 improved model accuracy by more than 50% for both heterotrophic and autotrophic growth. To generate even more realistic predictions of metabolic flux from genome-scale modeling, Raman spectroscopy was employed to help design biomass equations of microbial cells in different environmental conditions. To do this, the cellulose- consuming anaerobe Clostridium cellulolyticum ATCC 35319 was grown on cellobiose, and samples were obtained at different points of differentiation due to sporulation. Biomass composition was determined through Raman spectroscopy and traditional chemical analyses. A new genome-scale model of this organism (iCCE557) served as the basis for genome-scale model calculations. Model fitness improved upto 95% with these methods. Finally, to implement metabolic engineering strategies, regulatory RNA molecules (antisense RNAs) were designed to help target desired mRNA molecules in the metabolic network. Thermodynamic binding calculations were found to correlate with the efficiency of asRNA-mRNA binding and inhibition of mRNA translation. / Ph. D.