Signal transduction systems regulate the majority of cellular activities including the metabolism, development, host-recognition, biofilm production, virulence, and antibiotic resistance of human pathogens. Thus, knowledge of the proteins and interactions that comprise these communication networks is an essential component to furthering biomedical discovery. The proliferation of genomic data from high-throughput sequencing projects has dramatically accelerated biological discovery, yet there is an ever-increasing need for computational approaches to synthesize meaningful higher-order knowledge from this data. The inherent complexity of signal transduction systems has impeded efforts to elucidate the higher-order properties of these critical regulatory networks at the genomic level. The goal of the proposed research is to develop an effective, computational approach for deriving microbial signal transduction pathways from genomic data and linking these systems to their respective regulatory and metabolic targets. The proposed solution to computationally inferring signal transduction pathways involves a "bottom-up" approach that begins with analyzing signal transduction proteins at the domain level, followed by establishing clusters of orthologous domains, and finally grouping functionally associated two-component proteins into pathways using genomic context methods. The final product will be a comprehensive database of microbial signal transduction systems and their associated metabolic networks. Knowledge of the signaling systems responsible for the virulence of human pathogens will promote our understanding of bacteria-host interactions, the immune response, novel antibiotic drug discovery, and therapeutic applications for treating disease. Because of the critical role of signal transduction in bacteria, this project will significantly impact and contribute to biomedical discovery, public health care, bioremediation, and agriculture.
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