Metabolic networks are intricate systems comprising of interconnected biochemical reactions transforming source metabolites into target metabolites. This thesis presents a web-based tool called MAPPS: Metabolic network Analysis and Pathway Prediction Server (https://mapps.lums.edu.pk), for the prediction of metabolic pathways and comparisons of metabolic networks using traditional and ‘omics datasets. MAPPS provides an intuitive approach to answer biological questions focusing on the metabolic capabilities of an organism as well as differences between organisms or the evolution of different species by allowing pathway-based metabolic network comparisons at an organism as well as at a phylogenetic level. MAPPS also allows users to study the behavior of engineered metabolic networks and effects of metabolic availability/unavailability on metabolic pathways, identify potential drug targets, study host-microbe interactions, and build ancestral networks over a given phylogeny. MAPPS is used to understand the metabolic diversity and functional specialization in different strains of the bacteria belonging to genus Pseudomonas by performing whole-network and pathway-based comparisons relating to carbohydrate and energy metabolisms. Results suggest that pseudomonads with similar lifestyle tend to have a high degree of metabolic similarity and that species have adapted their metabolic networks to suit their diverse lifestyles. Finally, this thesis explores the changes occurring in the metabolic networks of two mango (Mangifera indica) cultivars, ‘Sindhri’ and ‘Kala Chaunsa’ during fruit maturation. For this, metabolic maps of various KEGG pathway maps are developed by assigning metabolic annotations to a mango transcriptomic reference, which are further used to analyze metabolic pathways differentially expressed between immature and mature stages in the two cultivars by identifying differentially expressed genes. Results suggest that carbohydrates, lipids and amino acids, and secondary metabolite pathways are differentially expressed in both cultivars, demonstrating the use of ‘omic data for better understanding of metabolic networks in today’s post-genomic era.