Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to\r\ncomputational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within\r\ntheir context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying\r\npotential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets,\r\nfunctional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of\r\nthese microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between\r\nMycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced\r\nfor identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of\r\npotential drug targets and to move towards optimal target-based strategies.
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