Inference and Testing of Quantitative Models of Genetic Interaction

描述（由申请人提供）：这项拟议的拨款奖励旨在使首席研究员（PI），一名受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提出了一个旨在构建和测试遗传相互作用定量模型的计划。微生物学、遗传学和基因组学专家Timothy Galitski博士将担任导师，基因组学和系统生物学专家Leroy Hood博士将担任联合导师。培训计划包括实验室培训、课程作业、研讨会和会议出席。提出的研究计划的目标是在一个模型系统中进行遗传相互作用的系统和定量分析。遗传相互作用分析，结合分子生物学，已经成功地用于产生基因和基因扰动如何在功能上相互作用以影响表型的假设。这些技术将用于研究酵母细胞从酵母状生长向病原体样侵入丝状生长的分化，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)开发预测性、定量的基因影响模型，并整合分子相互作用和表型数据；(2)实验测试模型推导的基因组表达模式和表型预测；(3)将建模方法扩展到杂交群体。实验结果将纳入初始数据集，以供后续分析和改进建模，以开发更广泛应用的计算方法。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。提出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。该模型的目标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设，并确定靶向治疗干预的特定分子候选物。

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.