Statistical Methods for Estimation of Gene Regulatory Networks

项目摘要/摘要 基因组技术的进步导致了许多基因的发现，这些基因的表达在 细胞条件；然而，基因并不是孤立地起作用，而是在复杂的网络中共同起作用 推动细胞功能。通过考虑基因(和基因产物)之间的相互作用，一个人获得了更多 深入了解潜在的细胞机制。对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 干杯干杯。癌症进展已被证明会导致基因网络的剧烈变化，这些基因网络对 细胞功能正常。一些致癌基因突变会在网络结构中产生自我维持的变化 因此，移除原始突变并不能恢复正常的细胞功能。这表明，识别 最初的致癌基因突变可能不利于进行有针对性的干预；相反，需要详细了解 正常和恶性细胞中都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毒性的主要工具。 对干预措施的孤立性反应。不幸的是，当前的网络估计算法不能充分地 根据表达数据重建基因网络。这并不令人惊讶，因为大多数网络估计通常- Rithms的功能是模块化的，不考虑前面步骤中的不确定性。拟议研究的总体目标 主要内容有：(1)通过扰动实验，改进基因调控网络的估计方法 明确地对过程的每一步进行建模并合并不确定性，以及(2)使用这些估计 用于预测细胞对干预反应的网络。 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。该奖项将提供支持，以获得所需的专业知识 解决拟议的研究目标，并过渡到独立的研究生涯。这将会实现的 通过课程作业、指导和研究经验的结合。特别重要的是继续 通过正规的基因组学课程和教学，我在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 开发应对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，在其中我可以获得成功所需的技能 向独立研究过渡。

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.