Bacterial pathogens are a major cause of diseases in human, agricultural plants and farm animals. Even after decades of research they remain a challenge to health care as they are known to rapidly evolve and develop resistance to the existing drugs. Systems biology is an emerging area of research where all of the components of the system, their interactions, and the dynamics can be studied in a comprehensive, quantitative, and integrative fashion to generate predictive models. When applied to bacterial pathogenesis, systems biology approaches will help identify potential novel molecular targets for drug discovery. A pre-requisite for conducting systems analysis is the identification of the building blocks of the system i.e. individual components of the system (structural annotation), identification of their functions (functional annotation) and identification of the interactions among the individual components (interaction prediction). In the context of bacterial pathogenesis, it is necessary to identify the host-pathogen interactions. This dissertation work describes computational resources that enable comprehensive systems level study of host pathogen system to enhance our understanding of bacterial pathogenesis. It specifically focuses on improving the structural and functional annotation of pathogen genomes as well as identifying host-pathogen interactions at a genome scale. The novel contributions of this dissertation towards systems biology of bacterial pathogens include three computational tools/resources. “TAAPP” (Tiling array analysis pipeline for prokaryotes) is a web based tool for the analysis of whole genome tiling array data for bacterial pathogens. TAAPP helps improve the structural annotation of bacterial genomes. “ISO-IEA” (Inferred from sequence orthology - Inferred from electronic annotation) is a tool that can be used for the functional annotation of any sequenced genome. “HPIDB” (Host pathogen interaction database) is developed with data a mining capability that includes host-pathogen interaction prediction. The new knowledge gained due to the implementation of these tools is the description of the non coding RNA as well as a computationally predicted host-pathogen interaction network for the human respiratory pathogen Streptococcus pneumoniae. In summary, the computation tools and resources developed in this dissertation study will enable building systems biology models of bacterial pathogens
