AF: EAGER: Bayesian Factor Modeling of Context-Specific Gene Regulation

AF：EAGER：上下文特定基因调控的贝叶斯因子建模

• 批准号: 1246073
• 负责人: Yufei Huang
• 金额: $ 29.69万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1246073&HistoricalAwards=false
• 关键词: AF; EAGER; Bayesian; Factor; Modeling

Response of cells to their changing environment is governed by intricate regulations of gene expression by regulating molecules in cells including, most importantly, transcription factors (TFs) and microRNAs (miRNAs). Understanding how TF and miRNA regulations define cellular states such as cell survival, cell proliferation, and cell death, and eventually phenotypes including various diseases is a major challenge facing computational systems biologists. Intellectual Merit This EAGER project will develop and validate a novel computational model called Semi-parametric Bayesian FActor Regulatory Model (SB-FARM) for TF and miRNA regulation of gene expression. The advantages of SB-FARM over other existing models are that it integrates existing knowledge about gene regulations in the model and enables the discovery of specific regulations by TFs and miRNAs under unmeasured conditions or contexts. The investigators will examine the modeling details as well as algorithms for reconstructing the model from a set of gene expression data. They will apply SB-FARM to study the context-specific regulations in E-coli and human cancer. The long term goal of the investigators is to develop signal processing and statistical learning methods for the system-level understanding of gene regulatory networks underlying different biological processes and apply them to better understand the genomic basis for diversity in organisms and development of diseases. The SB-FARM has a general structure that permits integration of additional aspects of gene regulation. The success of the SB-FARM will have long lasting impact on gene regulation research and is expected to also significantly advance statistical signal processing and Bayesian learning.Broader Impact This research is highly interdisciplinary, cross-cutting science and engineering. It will provide an environment for advanced interdisciplinary learning and education in the area of genomics signal processing and computational biology. The PIs will also actively involve graduate and undergraduate students in research activities. Particularly, they will utilize the minority institution status of UTSA and UTHSCSA to recruit and involve minority students in this research. The developed computational methods will be implemented into publically available software to aid computational biology researchers to investigate context specific gene regulations. The computational methods and tools will enhance the signal processing and machine learning research and ultimately lead to development of new theory and methods.

细胞对环境变化的反应是由复杂的基因表达调控所控制的，调控分子包括细胞中最重要的转录因子（TFs）和microrna （miRNAs）。理解TF和miRNA调控如何定义细胞状态，如细胞存活、细胞增殖和细胞死亡，以及最终包括各种疾病在内的表型，是计算系统生物学家面临的主要挑战。该EAGER项目将开发并验证一种称为半参数贝叶斯因子调控模型（SB-FARM）的新型计算模型，用于TF和miRNA对基因表达的调控。SB-FARM相对于其他现有模型的优势在于，它在模型中整合了有关基因调控的现有知识，并能够在未测量的条件或背景下发现tf和mirna的特定调控。研究人员将检查建模细节以及从一组基因表达数据重建模型的算法。他们将应用SB-FARM来研究大肠杆菌和人类癌症的具体情况。研究人员的长期目标是开发信号处理和统计学习方法，用于系统级理解不同生物过程背后的基因调控网络，并将其应用于更好地理解生物多样性和疾病发展的基因组基础。SB-FARM具有允许整合基因调控其他方面的一般结构。SB-FARM的成功将对基因调控研究产生持久的影响，并有望显著推进统计信号处理和贝叶斯学习。本研究是高度跨学科、跨领域的科学与工程。它将为基因组学、信号处理和计算生物学领域的高级跨学科学习和教育提供一个环境。pi还将积极地让研究生和本科生参与研究活动。特别是，他们将利用UTSA和UTHSCSA的少数民族机构地位来招募少数民族学生并让他们参与这项研究。开发的计算方法将被实施到公共软件中，以帮助计算生物学研究人员调查特定环境的基因调控。计算方法和工具将加强信号处理和机器学习的研究，并最终导致新的理论和方法的发展。