Learning Conditionally Essential Genetic Networks in the Protein Homeostasis System

大规模并行突变和深度DNA测序方面的最新技术进步使 研究人员将在什么条件下发现复杂细胞系统中的基本遗传网络 这些遗传网络是必不可少的。但识别这种有条件的基本网络(CEN)具有 由于计算和统计方面的原因，一直具有挑战性。这个项目的目标是阐明和 通过开发高效的计算和统计方法来验证蛋白质稳态系统中的CENS 从大规模平行突变中分析深度DNA测序数据的准确方法 实验。蛋白质动态平衡系统的失调会导致蛋白质组的不平衡 会导致神经退行性病变，如阿尔茨海默氏症、亨廷顿氏症或帕金森氏症。 开发一种在蛋白质动态平衡系统中学习cens的方法将导致更好的 对这一复杂系统的基本理解，并将为综合疗法提供信息 神经退行性疾病。更广泛地说，这是一种统计上严格的分析大规模并行的工具 诱变实验将使研究人员能够在其他复杂的分子系统中发现Cen。 团队已经做好了充分的准备来完成这个项目的具体目标，因为他们的初步 非参数贝叶斯模型的发展，其初步实验数据来自于蛋白质 动态平衡系统，他们在开发统计模型以从基因组数据中学习的经验， 他们共同合作研究的记录，以及计算和实验环境 在他们的机构里。为了完成总体目标，团队将完成以下具体工作 目标：(1)开发并验证一个非参数贝叶斯模型，用于从大规模的CEN中识别 并行突变深度测序实验，以及(2)鉴定和验证蛋白质动态平衡 利用转座子进行测序实验。这个项目将创造新的统计方法、模型、 以及用于分析来自大规模平行的大量和纯化样本的DNA测序数据的软件 突变实验，以发现潜在的条件基本网络。本论文的研究目的是 该项目将促进对非参数贝叶斯统计分析和蛋白质动态平衡的理解 分子生物学，这些研究目的直接与更广泛的影响相联系，从而推动 妇女和少数群体参与STEM领域，并改善社会个人的福祉。 与马萨诸塞州霍利奥克的Girls，Inc.合作，将举办一个名为《我的DNA，我的药》的研讨会 旨在鼓励初中生参与统计、计算机科学、 和遗传学。

Recent technological advances in massively parallel mutagenesis and deep DNA sequencing are enabling researchers to discover essential genetic networks in complex cellular systems and under what conditions those genetic networks are essential. But identifying such conditionally essential networks (CENs) has been challenging for computational and statistical reasons. The goal of this project is to elucidate and validate CENs in the protein homeostasis system by developing computationally efficient and statistically accurate methods for analyzing deep DNA sequencing data from massively parallel mutagenesis experiments. Dysregulation of the protein homeostasis system leads to imbalances in the proteome which can cause neurodegenerative pathologies such as Alzheimer's, Huntington's, or Parkinson's disease. Developing a method for learning CENs in the protein homeostasis system will lead to a better fundamental understanding of this complex system and will inform combination therapeutics for neurodegenerative diseases. More broadly, a statistically rigorous tool for analyzing massively parallel mutagenesis experiments would allow researchers to discover CENs in other complex molecular systems. The team is well-prepared to complete the specific aims of this project because of their preliminary nonparametric Bayesian model development, their preliminary experimental data from the the protein homeostasis system, their experience with developing statistical models for learning from genomic data, their track record of collaborative research together, and the computational and experimental enviromnent at their institution. To complete the overall objective, the team will accomplish the following specific aims: (1) develop and validate a nonparametric Bayesian model for identifying CENs from massively parallel mutagenesis deep sequencing experiments, and (2) identify and validate protein homeostasis CENs using transposon sequencing experiments. This project will create new statistical methods, models, and software for analyzing DNA sequencing data from bulk and purified samples from massively parallel mutagenesis experiments to discover latent conditionally essential networks. The research aims of this project will advance understanding of nonparametric Bayesian statistical analysis and protein homeostasis molecular biology, and those research aims connect directly to broader impacts that advance the full participation of women and minorities in STEM fields and improve well-being of individuals in society. In partnership with Girls, Inc of Holyoke, MA, a workshop titled "My DNA, My Medicine" will be developed to encourage participation of middle and high school students in statistics, computer science, and genetics.