Quantitative Characterization & Modeling of sRNA-Mediated Gene Regulation

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DESCRIPTION (provided by applicant): In recent years, there has been a growing appreciation for the complex roles that small regulatory RNA (sRNA) can play in coordinating gene activities in both prokaryotes and eukaryotes. There exists by now a basic understanding of the molecular components and mechanisms involved in sRNA-mediated gene regulation in bacteria. Theoretical analysis by our lab suggest a number of unique functional features for sRNA-mediated gene regulation, including a threshold-linear response and a resistance to noisy fluctuation, with interesting implications on properties of genetic circuits involving sRNA. Here we propose a joint experimental/computational research program to characterize the functions of sRNA in E. coli at multiple scales. At the molecular scale, we will elucidate the sequence determinant of sRNA-target interaction for the most common RyhB-class sRNA by characterizing the gene expression of selected mutants, and constructing biopysical/bioinformatic models of the interaction. At the "device" scale, we will characterize various novel properties predicted for sRNA-mediated regulation, including hierarchical cross talk between different targets of the same sRNA. At the circuit scale, we will construct synthetic circuits and study the properties of common circuit motifs such as the cascade and switches, and investigate their temporal characteristics. In addition we will develop models to design antisense sRNAthat target and silence specific host genes. We expect that the knowledge of the molecular interaction to lead to predictive tools to identify the potentially large number of sRNA targets and construct additional layers of the gene regulatory network involving sRNA in E. coli, while knowledge of the devices and common circuit motifs will shed light towards understanding the special roles that sRNA regulators may play in coordinating larger scale networks. Additionally, the silencing of endogenous target may lead to an effective tool to generate multiple gene knockdown strains which may be used for drug discover studies as well as gene- gene interaction studies at the genome-wide scale.