Statistical methods for integromics discoveries

整合组学发现的统计方法

• 批准号: 7740132
• 负责人: XINGHUA LU
• 金额: $ 31.8万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 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 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7740132
• 关键词: Acquired Immunodeficiency Syndrome; Address; Binding Sites; Biological; Biomedical Research; Biotechnology; Cells; Collection; Complex; Computing Methodologies; Data; Disease; Environment; Etiology; Gene Expression; Gene Proteins; Genes; Genomics; Goals; Human body; Individual; Information Systems; Information Theory; Lead; Learning; Malignant Neoplasms; Methodology; Methods; Modeling; Organ; Physiology; Play; Process; Recovery; Role; Signal Transduction; Signal Transduction Pathway; Signaling Molecule; Statistical Methods; Statistical Models; System; Systems Biology; Techniques; Testing; Yeasts; base; biological systems; computerized tools; epigenomics; genome sequencing; high throughput technology; human disease; insight; metabolomics; model design; novel; tool; transcription factor; transcriptomics

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改变生物医学研究的范式，从个体基因/蛋白质的最低限度的研究到系统水平的信息整合。当前的高通量生物技术使得能够收集大量的生物信息，并且细胞系统的不同方面用异质数据反映，例如，基因组学、表观基因组学、转录组学和代谢组学。然而，它仍然是一个重大的挑战，系统地整合这一机构的信息，并在机械水平上获得生物学的见解。该项目的总体目标是开发一个计算系统，该系统能够整合各种高通量“组学”数据（一种“智能”方法），以深入了解细胞系统，特别是信号转导系统。该项目的活动分为四个具体目标，从捕获多组学数据中的一般信息的方法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，以识别在大规模不同“组学”数据之间传递生物信息的信息模块。其次，我们开发的方法，以进一步调查，如果从多个组学数据的信息反映因果关系。第三，我们将开发工具来恢复系统中丢失的信息，以增强高吞吐量技术。最后，我们将开发一个统一的模型来阐明信号转导途径，通过整合信息形成多个组学数据的方式，这是生物学上明智的和数学上严格的。我们希望在该项目中开发的方法是广泛适用于研究各种细胞信号转导系统。