Statistical Methods for Estimation of Gene Regulatory Networks

基因调控网络估计的统计方法

• 批准号: 8580590
• 负责人: MATTHEW Nicholson MCCALL
• 金额: $ 7.99万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-10 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8580590
• 关键词: Address; Algorithms; Area; Award; 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; anticancer research; cancer cell; cancer genetics; cancer genomics; career; cell type; computer based statistical methods; density; experience; improved; network models; public health relevance; reconstruction; research study; response; skills; statistics; therapy design; tool; tumor progression

DESCRIPTION (provided by applicant): Advances in genomic technology have led to the discovery of numerous genes whose expression differs 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 differences 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 sufficient 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 cellular 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 algorithms 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 fields 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.

描述（由申请人提供）：基因组技术的进步导致发现了许多基因，其表达在细胞条件之间有所不同。但是，基因并非孤立起作用，而是在驱动细胞功能的复杂网络中一起起作用。通过考虑基因（和基因产物）之间的相互作用，人们对潜在的细胞机制有了更深入的了解。这些基因调节网络的估计对于了解细胞机制，检测细胞类型之间的差异以及预测细胞对干预措施的反应是必要的。癌症进展已显示出对正常细胞功能至关重要的遗传网络的急剧变化。某些致癌突变会在网络结构中产生自我维持的改变，因此去除原始突变不会恢复正常的细胞功能。这表明鉴定原始的致癌突变可能不足以进行靶向干预。相反，可能需要对正常和恶性细胞中存在的基因调节网络的详细理解。 基因扰动实验是研究基因调节网络并预测细胞对干预措施的反应的主要工具。不幸的是，当前的网络估计算法无法从表达数据中充分重建基因网络。鉴于大多数网络估计算法模块化函数，并且在先前的步骤中忽略了不确定性，这并不奇怪。拟议的研究的总体目标是：（1）通过使用明确模拟并在过程的每个步骤中纳入不确定性的方法来改善基因调节网络的估计，以及（2）使用这些估计的网络来预测细胞对干预的反应。 我的长期目标是借鉴统计，系统生物学和遗传学领域的复杂细胞网络的独立研究。该奖项将提供支持，以获取解决拟议的研究目的并过渡到独立研究职业所需的专业知识。这将通过课程，指导和研究经验的结合来实现。特别重要的是，通过正式的课程和基因组实验室技术的教学，继续我在分子生物学和癌症基因组学方面的教育。这将提供与生物医学研究人员紧密合作的必要背景，开发统计方法，以解决基因组研究中的尖端挑战。与我的导师和合作者的定期互动{统计学，计算生物学，生物医学遗传学和癌症研究专家{将提供丰富的环境，在其中我可以获得成功过渡到独立研究的必要技能。