Modulation of gonadotrope signaling by GnRH and its analogs is central to the treatment of a wide spectrum of diseases, including infertility and gonadal hormone-dependent tumors. During normal reproductive function, the gonadotrope interprets the pattern of GnRH and other extracellular stimuli in order to properly regulate gonadotropin biosynthesis. During the previous funding period, we have developed and applied multivariate systems biology assay, data acquisition and simulation techniques in order to understand the function of the gonadotrope as a molecular data processing network. We have developed high throughput quantitative genomics techniques and studied the global transcriptional program modulated by GnRH, the connections between signaling pathways and the transcriptome, the frequency dependence of continuous modular differential equation models and the characteristics of gonadotrope signal processing at the single cell level. We now propose the following aims that will generate a comprehensive model of gonadotrope molecular behavior in response to physiologically relevant inputs such as pulsed GnRH. 1 Experimentally refine the topological map of the gonadotrope signaling and gene network and develop a community-directed, pathway-based gonadotrope signaling map and knowledgebase. 2) Develop a scalable superfusion instrument to study large numbers of samples and single cell signaling/gene responses to GnRH frequency and its modulation by multiple stimuli and develop mathematical representations to test hypotheses about gonadotrope signal processing and frequency decoding. We will elucidate the mechanism of the GnRH frequency response and integration with other inputs and provide a solid basis for predictive mathematical modeling. These studies will provide fundamental insight into the mechanisms used by the gonadotrope's signaling network to control biosynthesis.
The treatment of many medical conditions, including infertility and reproductive system cancers, involves effects at the gonadotrope cell in the pituitary gland. The goal of this research is to generate detailed datasets and predictive models of these cells to help improve the treatment of these conditions.
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