Statistical Methods for Estimation of Gene Regulatory Networks

基因调控网络估计的统计方法

• 批准号: 8897013
• 负责人: MATTHEW Nicholson MCCALL
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8897013
• 关键词: Address; Algorithms; Area; Award; Bayesian Modeling; Bioinformatics; Biological; Cell physiology; Collaborations; Complex; Computational Biology; Data; Education; Environment; Excision; Future; Gene Expression; Genes; Genetic; Genomics; Goals; Instruction; Intervention; Knowledge; Laboratories; Laboratory Procedures; Laboratory Scientists; Mentors; Mentorship; Methodology; Methods; Modeling; Molecular Biology; Mutation; Noise; Normal Cell; Oncogenic; Phase; Process; Proteins; Regulator Genes; Reporting; Research; Research Personnel; Source; Space Models; Statistical Methods; Structure; Systems Biology; Techniques; Technology; Uncertainty; Variant; Work; abstracting; anticancer research; cancer cell; cancer genetics; cancer genomics; career; cell type; density; experience; improved; network models; reconstruction; research study; response; skills; statistics; therapy design; tool; tumor progression

Project Summary/Abstract Advances in genomic technology have led to the discovery of numerous genes whose expression di ers between cellular conditions; however, genes do not act in isolation, rather they act together in complex networks that drive cellular function. By considering the interactions between genes (and gene products), one gains a more in-depth understanding of the underlying cellular mechanisms. Estimation of these gene regulatory networks is necessary to understand cellular mechanisms, detect di erences between cell types, and predict cellular response to interventions. Cancer progression has been shown to produce drastic changes in genetic networks critical to normal cellular function. Some oncogenic mutations produce self-sustaining alterations in the network structure such that removal of the original mutation does not restore normal cellular function. This suggests that identifying the original oncogenic mutation may not be sucient for a targeted intervention; rather, a detailed understanding of the gene regulatory networks present in both normal and malignant cells may be necessary. Gene perturbation experiments are the primary tool to investigate gene regulatory networks and predict cel- lular response to interventions. Unfortunately, current network estimation algorithms are unable to adequately reconstruct gene networks from expression data. This is not surprising given that most network estimation algo- rithms function modularly and disregard uncertainty in previous steps. The overall goals of the proposed research are: (1) to improve the estimation of gene regulatory networks from perturbation experiments, by using methods that explicitly model and incorporate uncertainty in each step of the process, and (2) to use these estimated networks to predict cellular response to intervention. My long term goal is to pursue independent research into complex cellular networks drawing on the elds of statistics, systems biology, and genetics. This Award will provide support to obtain the expertise required to address the proposed research aims and transition to an independent research career. This will be accomplished through a combination of coursework, mentorship, and research experience. Of particular importance is continuing my education in molecular biology and cancer genomics through formal coursework and instruction in genomic laboratory techniques. This will provide the background necessary to work closely with biomedical investigators developing statistical methodology that addresses cutting-edge challenges in genomic research. Regular interaction with my mentors and collaborators { experts in Statistics, Computational Biology, Biomedical Genetics, and Cancer Research { will provide a rich environment in which I can obtain the necessary skills to successfully transition to independent research.

项目概要/摘要 基因组技术的进步导致发现了许多基因，其表达在不同基因之间存在差异。 细胞状况；然而，基因并不是孤立地起作用，而是在复杂的网络中一起起作用 驱动细胞功能。通过考虑基因（和基因产物）之间的相互作用，人们可以获得更多 深入了解潜在的细胞机制。这些基因调控网络的估计是 了解细胞机制、检测细胞类型之间的差异和预测细胞反应所必需的 进行干预。癌症进展已被证明会导致基因网络发生巨大变化，这对癌症至关重要 正常的细胞功能。一些致癌突变会导致网络结构发生自我维持的改变 这样，去除原始突变并不能恢复正常的细胞功能。这表明识别 最初的致癌突变可能不足以进行有针对性的干预；而是详细的了解 正常细胞和恶性细胞中存在的基因调控网络的研究可能是必要的。 基因扰动实验是研究基因调控网络和预测细胞的主要工具。 对干预措施的反应。不幸的是，当前的网络估计算法无法充分 从表达数据重建基因网络。鉴于大多数网络估计算法，这并不奇怪 算法以模块化方式运行，并忽略先前步骤中的不确定性。拟议研究的总体目标 是：（1）通过使用方法改进微扰实验对基因调控网络的估计 明确建模并在过程的每个步骤中纳入不确定性，以及（2）使用这些估计的 网络来预测细胞对干预的反应。 我的长期目标是利用以下领域对复杂的蜂窝网络进行独立研究 统计学、系统生物学和遗传学。该奖项将为获得所需的专业知识提供支持 解决拟议的研究目标并过渡到独立研究生涯。这将实现 通过课程作业、指导和研究经验的结合。特别重要的是继续 我通过基因组学的正式课程和指导接受了分子生物学和癌症基因组学方面的教育 实验室技术。这将为与生物医学研究人员密切合作提供必要的背景 开发解决基因组研究前沿挑战的统计方法。定期互动 与我的导师和合作者{统计学、计算生物学、生物医学遗传学和 癌症研究{将提供一个丰富的环境，使我能够获得成功所需的技能 向独立研究过渡。