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）：