Inference and Testing of Quantitative Models of Genetic Interaction

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DESCRIPTION (provided by applicant): This proposed grant award is designed to enable the principle investigator (PI), a theoretical physicist by training, to develop into an independent researcher in systems biology. The PI proposes a program aimed at constructing and testing quantitative models of genetic interaction. Dr. Timothy Galitski, an expert in microbiology, genetics, and genomics, will serve as mentor and Dr. Leroy Hood, an expert in genomics and systems biology, will serve as co-mentor. The training program includes laboratory training, course work, seminars, and conference attendance. The goal of the proposed research program is to carry out a systematic and quantitative analysis of genetic interactions in a model system. Genetic interaction analysis, in combination with molecular biology, has been successfully used to generate hypotheses of how genes and gene perturbations functionally interact to affect phenotypes. These techniques will be employed to study the differentiation of yeast cells from yeast- form growth to the pathogen-like invasive filamentous form, a major prototype in the study of cell differentiation. The research program is divided into three specific aims: (1) Develop predictive, quantitative gene-influence models and integrate with molecular-interaction and phenotype data; (2) Experimentally test model-derived predictions of genomic expression patterns and phenotypes; and (3) Extend modeling methods to crossbred populations. Experimental results will be incorporated into the initial datasets for subsequent rounds of analysis and refined modeling to develop computational methods for wider application. Understanding the effects of genetic diversity on human health and disease will require not only identifying trait genes, but also understanding how they functionally combine to affect a phenotype or clinical outcome. The proposed research aims to facilitate this understanding by developing models of genetic interaction integrated with physical interaction data. The goal of the modeling is to predict the effects of interacting genetic perturbations, allowing for the formulation and testing of polygenic hypotheses and identification of specific molecular candidates for targeted therapeutic intervention.