Statistical methods for integromics discoveries

整合组学发现的统计方法

• 批准号: 8131525
• 负责人: XINGHUA LU
• 金额: $ 32.62万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8131525
• 关键词: Statistical Methods

DESCRIPTION (provided by applicant): Contemporary systems biology is shifting the paradigm of biomedical research from minimalistic studies of individual genes/proteins to integration of information at systems level. Current high throughput biotechnologies enable collection of a large amount of biological information, and the different aspects of the cellular systems are reflected with heterogeneous data, e.g., genomics, epigenomics, transcriptomics and metabolomics. However, it remains a major challenge to systematically integrate this body of information and derive biological insights at a mechanistic level. The overarching goal of this project is to develop a computational system that enables integration of various high throughput "omics" data (an "integromics" approach) to gain insights into cellular systems, in particular the signal transduction systems. The activities of the project are organized into four specific aims, which progress from approaches for capturing general information among the multiple omics data to more specific and complex models designed to decipher specific cellular signaling systems. Firstly, we will develop a general framework, based on information theory and probabilistic models, to identify information modules that convey biological information between different "omics" data at large scale. Secondly, we develop methods to further investigate if the information from the multiple omics data reflects causal relationships. Thirdly, we will develop tools to recover missing information from the system to augment the high throughput technologies. Finally, we will develop a unified model to elucidate signal transduction pathways by integrating information form multiple omics data in manner that is both biologically sensible and mathematically rigorous. We expect that the methodologies developed in the project are widely applicable to study a variety of cellular signal transduction systems.

描述(由申请人提供)： 当代系统生物学正在将生物医学研究的范式从对单个基因/蛋白质的最低限度的研究转变为在系统水平上的信息集成。目前的高通量生物技术能够收集大量的生物信息，细胞系统的不同方面通过不同的数据反映出来，例如基因组学、表观基因组学、转录组学和代谢组学。然而，系统地整合这一信息体并在机制层面上获得生物学见解仍然是一个重大挑战。该项目的总体目标是开发一种计算系统，能够集成各种高通量的“组学”数据(一种“集成”方法)，以深入了解蜂窝系统，特别是信号转导系统。该项目的活动被组织成四个具体的目标，从捕获多重组学数据中的一般信息的方法发展到旨在破译特定细胞信号系统的更具体和复杂的模型。首先，我们将开发一个基于信息论和概率模型的通用框架，以识别在不同的组学数据之间大规模传递生物信息的信息模块。其次，我们开发了进一步调查来自多重组学数据的信息是否反映因果关系的方法。第三，我们将开发工具，从系统中恢复丢失的信息，以增强高通量技术。最后，我们将开发一个统一的模型来阐明信号转导途径，通过以生物敏感和数学严格的方式整合来自多个组学数据的信息。我们期望该项目中开发的方法学能够广泛应用于各种细胞信号转导系统的研究。