Genome-scale metabolic reconstructions have become indispensable tools for understanding and investigation of the metabolism of any organism in this post-genomic era. However, there are still knowledge gaps in our understanding of the metabolism of even the most well-studied organism, hence a gapfilling algorithm based on novel reaction generating framework was implemented and shown to be able to refine the reconstruction as well as improve our knowledge of the network. The analysis of these large-scale models has their own set of challenges and difficulties that limit their full utility in practical application areas and higher usage by industry and experimentalists. Constraint-based analyses of metabolic models are highly popular methods, as it only requires knowledge of stoichiometry and key input/output fluxes of the system to infer the underlying intracellular fluxes. However due to the underdetermined nature of most large metabolic networks, additional constraints have to be incorporated to ascertain the actual state of the network. Thermodynamic constraints eliminate thermodynamically infeasible solutions and also provide another layer of information onto metabolic networks. Using Thermodynamics-based Flux Balance Analysis (TFBA) of metabolic models and taking into account pH and ionic strength differences, we study how these factors can affect overall cellular energetics. TFBA also enables us to integrate metabolomics data to reduce the solution space of the problem. As genome-scale networks have too many degrees of freedom for systematic analysis and conceptually too difficult to manage, a consistent approach of reducing genome-scale models to core metabolic models that retain most of the network characteristics of the original model is proposed. Such core metabolic models are valuable resources for simplifying analysis that can be extrapolated to the original model. A novel framework for characterizing the flux and thermodynamic state of metabolic networks is proposed in order to systematically analyze the possible intracellular states given the set of known parameters. The characterized states can be further analyzed using metabolic control analysis to provide insights as to how the control of the network is distributed and also provide a bridge to the formulation of large-scale kinetic models. We demonstrate how these computational methods are valuable tools for the integration of experimental data and interrogation of complex metabolic networks and how their applications in studying the interaction of micro-organisms in their environment in silica.