Computational Strategies for Quantitative Mapping of Genetic Interaction Networks

遗传相互作用网络定量作图的计算策略

• 批准号: 8133157
• 负责人: Chad L Myers
• 金额: $ 21.87万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-25 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8133157
• 关键词: Accounting; Affect; Alleles; Animal Model; Area; Attention; Biological; Biological Assay; Biological Process; Buffers; Cell physiology; Cells; Chad; Chromosome Mapping; Collaborations; Communities; Complex; Computer Analysis; Computing Methodologies; Coronary heart disease; Coupling; Data; Data Set; Development; Disease; Engineering; Evaluation; Feedback; Gene Structure; Genes; Genetic; Genetic Models; Genetic Variation; Genome; Genomics; Goals; Gold; Growth; Hybrids; Imagery; Internet; Investigation; Knowledge; Machine Learning; Maps; Measures; Medical; Methods; Modeling; Molecular; Molecular Biology; Mutation; Organism; Outcome; Phenotype; Plague; Population; Property; Proteins; Proteomics; Quantitative Genetics; Recommendation; Research; Resources; Sensitivity and Specificity; Structure; Study Section; Systems Biology; Technology; Training; Work; Yeasts; base; combinatorial; computer framework; data integration; disease phenotype; effective therapy; gene function; genetic variant; genome wide association study; high throughput technology; human disease; innovation; insight; lens; mutant; network models; novel; prototype; public health relevance; research study; yeast genetics

DESCRIPTION (provided by applicant): Recent studies suggest that many diseases, particularly those that commonly afflict our population, result from interactions among multiple alleles. In an attempt to understand these complex phenotypes, recent experimental efforts in model organisms have focused on measuring such interactions by engineering combinatorial genetic perturbations. Due to the enormous space of possible mutants, brute-force experimental investigation is simply not feasible, and thus, there is a critical need for computational strategies for intelligent exploration of genetic interaction networks. The specific objective of this application is to develop a computational framework for leveraging the existing genomic or proteomic data to enable intelligent direction of combinatorial perturbation studies. The rationale for the proposed research is that although current knowledge of genetic interactions is sparse, the integration of existing genomic and proteomic data can enable the inference of network models that suggest promising candidates for high-throughput interaction screens. Using such computational guidance should enable more efficient characterization of network structure, and ultimately, better understanding of how genes contribute to complex phenotypes. Based on strong findings in preliminary studies, this objective will be accomplished through two specific aims: (1) development of critical normalization methods and quantitative models for colony array-based interaction assays, and (2) novel machine learning-based approaches for iterative model refinement and optimal interaction screen selection. The proposed research is innovative because it would represent one of the first efforts to couple genomic data integration and network inference technology with a large-scale experimental effort, where several months of experimental investigation are based entirely on computational direction. Such an approach will yield insight into how combinatorial perturbations can be used to characterize global modularity and organization, and more generally, would serve as a prototype for hybrid computational-experimental strategies in other genomic contexts. PUBLIC HEALTH RELEVANCE: Many common diseases result from interactions among multiple genes. One approach to studying multigenic interactions is to introduce combinations of mutations in model organisms and observe how they affect the cell. This project proposes to develop computational strategies to guide and interpret these combinatorial perturbation studies, which will ultimately help us better understand and treat multigenic diseases.

描述（由申请人提供）：最近的研究表明，许多疾病，特别是那些通常困扰我们的人口，是由多个等位基因之间的相互作用。在试图了解这些复杂的表型，最近在模式生物的实验努力集中在测量这种相互作用的工程组合遗传扰动。由于可能的突变体的巨大空间，蛮力实验研究是根本不可行的，因此，有一个关键的需要计算策略的智能探索的遗传相互作用网络。本申请的具体目标是开发一个计算框架，用于利用现有的基因组或蛋白质组数据，以实现组合扰动研究的智能方向。拟议研究的基本原理是，尽管目前对遗传相互作用的了解很少，但现有基因组和蛋白质组数据的整合可以使网络模型的推断成为可能，这些模型为高通量相互作用筛选提供了有希望的候选者。使用这种计算指导应该能够更有效地表征网络结构，并最终更好地理解基因如何促成复杂的表型。 基于初步研究的有力发现，这一目标将通过两个具体目标来实现：（1）为基于菌落阵列的相互作用测定开发关键归一化方法和定量模型，以及（2）用于迭代模型优化和最佳相互作用筛选的基于机器学习的新方法。 拟议的研究是创新的，因为它将代表将基因组数据集成和网络推理技术与大规模实验工作相结合的首批努力之一，其中几个月的实验研究完全基于计算方向。这种方法将深入了解如何组合扰动可以用来表征全球模块化和组织，更一般地说，将作为一个原型的混合计算实验策略在其他基因组环境。 公共卫生相关性：许多常见疾病是由多个基因之间的相互作用引起的。研究多基因相互作用的一种方法是在模式生物中引入突变的组合，并观察它们如何影响细胞。该项目建议开发计算策略来指导和解释这些组合扰动研究，这将最终帮助我们更好地理解和治疗多基因疾病。