Inference and Testing of Quantitative Models of Genetic Interaction

描述(由申请人提供)：这项拟议的拨款旨在使首席研究员(PI)，一个经过培训的理论物理学家，发展成为系统生物学的独立研究人员。PI提出了一项旨在构建和测试遗传交互作用的量化模型的计划。微生物学、遗传学和基因组学专家蒂莫西·加里茨基博士将担任导师，基因组学和系统生物学专家勒罗伊·胡德博士将担任共同导师。培训计划包括实验室培训、课程工作、研讨会和出席会议。拟议的研究计划的目标是对模型系统中的遗传交互作用进行系统和定量的分析。遗传相互作用分析与分子生物学相结合，已成功地用于产生关于基因和基因扰动如何功能相互作用以影响表型的假说。这些技术将被用于研究酵母细胞从酵母状生长到病原体样入侵丝状的分化，这是细胞分化研究的主要原型。该研究计划分为三个具体目标：(1)开发预测的、定量的基因影响模型，并与分子相互作用和表型数据相结合；(2)通过实验测试模型对基因组表达模式和表型的预测；以及(3)将建模方法扩展到杂交群体。实验结果将被纳入初始数据集，用于随后的几轮分析和精细化建模，以开发更广泛应用的计算方法。要了解遗传多样性对人类健康和疾病的影响，不仅需要识别特征基因，还需要了解它们如何在功能上结合起来影响表型或临床结果。拟议的研究旨在通过开发与物理相互作用数据相结合的遗传相互作用模型来促进这种理解。建模的目的是预测相互作用的遗传扰动的影响，允许制定和测试多基因假说，并确定特定的候选分子进行靶向治疗干预。

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.