This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The RNA interference (RNAi) process is a recently discovered cellular mechanism that plays key roles in genetic-level immune response and genetic regulation. The immense excitement that RNAi is currently arousing in the pharmaceutical and biology communities stems from its wide spectrum of potential therapeutic applications including the treatment of cancer, HIV, high blood pressure, macular degeneration, Huntington's disease, and a myriad of other serious ailments. Further, RNAi appears to be an unprecedently powerful tool for genomic research that is ushering in a new generation of genetic science. In spite of the visible successes of RNAi, and the high level of excitement surrounding it, there are a number of critical open questions that must be answered to transition this process into a reliable, robust, and safe technology. In particular, we need improved predictive models of RNAi efficacy, better models of and controls for nonspecific ('off-target' or 'false positive') effects, network models of the RNAi regulatory system, and informatic support tools for designing RNAi-based knockdowns. The grand vision is a complete model of the biochemical processes of the whole RNAi pathway and its interaction with coding and non-coding sections of the genome. My research group is working on a number of research directions toward this vision, including siRNA efficacy prediction, off-target analysis, gene family knockdown design, and high performance string analysis algorithms (to support the previous directions). The results of our studies include improved understanding of the biology of RNAi, models and algorithms that allow RNAi practitioners to better predict and control the results of RNAi interventions, and a suite of publicly available software tools that implement these models.
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