Inference and Testing of Quantitative Models of Genetic Interaction

遗传相互作用定量模型的推理和检验

• 批准号: 7497609
• 负责人: Gregory W Carter
• 金额: $ 10.66万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-20 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7497609
• 关键词: Affect; Alleles; Award; Binding; Biological Assay; Biological Models; Cell Differentiation process; Cells; Classification; Clinical; Complex; Computing Methodologies; Crossbreeding; Data; Data Set; Disease; Drug Formulations; Gene Expression; Genes; Genetic; Genetic Models; Genetic Variation; Genomics; Genotype; Goals; Grant; Growth; Health; Human; Invasive; Laboratories; Link; Mediating; Mentors; Methods; Microbiology; Modeling; Molecular; Molecular Biology; Molecular Profiling; Outcome; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Plug-in; Population; Regulation; Research; Research Personnel; Staging; Statistical Models; Systems Biology; Techniques; Testing; Therapeutic Intervention; Training; Training Programs; Transcript; Variant; Work; Yeasts; base; design; genetic analysis; genetic variant; network models; open source; pathogen; programs; prototype; research study; response; symposium; trait; transcription factor

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.

描述（由申请人提供）：这个拟议的补助金旨在使主要研究者（PI），理论物理学家的培训，发展成为一个独立的研究人员在系统生物学。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蒂莫西·加利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，研讨会和会议出席。该研究计划的目标是对模型系统中的遗传相互作用进行系统和定量分析。遗传相互作用分析与分子生物学相结合，已成功地用于产生基因和基因扰动如何在功能上相互作用以影响表型的假说。这些技术将用于研究酵母细胞从酵母形式生长到病原体样侵入性丝状形式（细胞分化研究中的主要原型）的分化。该研究计划分为三个具体目标：（1）开发预测性的定量基因影响模型，并与分子相互作用和表型数据相结合;（2）实验测试模型衍生的基因组表达模式和表型预测;（3）将建模方法扩展到杂交种群。实验结果将被纳入初始数据集，用于随后的分析和改进建模，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性状基因，还需要了解它们如何在功能上联合收割机影响表型或临床结果。拟议中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。建模的目标是预测相互作用的遗传扰动的影响，允许多基因假说的制定和测试，并确定特定的分子候选人有针对性的治疗干预。