CAREER: Dissecting the Mechanisms of Genetic Control of Biological Systems via High-Dimensional Sparse Graphical Models

职业：通过高维稀疏图形模型剖析生物系统遗传控制机制

• 批准号: 1149885
• 负责人: Seyoung Kim
• 金额: $ 92.72万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149885&HistoricalAwards=false
• 关键词: CAREER; Dissecting; Mechanisms; Genetic; Control

Since the completion of genome sequencing projects for various organisms including human and other model organisms, the fundamental goal of research in computational genomics, systems biology, and genetics has been to gain a complete understanding of how the instruction sets encoded in genomes get executed within a cell system and organism. The recent advances in the high-throughput technology and next-generation sequencing technology have allowed the researchers to collect a large amount of data for the genomes and various other aspects of a cell system. Such datasets hold the key to understanding the detailed mechanisms of the genetic control of a biological system and further deepening our knowledge of cell biology with the potential for broad application. This project will develop statistical machine learning methods based on high-dimensional sparse graphical models for integrative analysis of genomic datasets. As graphical models provide a powerful tool for representing the complex structure of the unknown biological processes that underlie the observed genomic data, the computational methods to be developed in this project will be able to extract rich information on the genetic control of gene regulation systems from genome-scale datasets.This project will also include training the next-generation computational biologists by supervising graduate students and incorporating the research results into the curriculum. The project will involve collaboration between computational scientists and biologists to participate in outreach programs for high-school students to present them an alternative career path that combines biological and computer sciences. In addition, the project will contribute to increasing womens participation in science and engineering.

自从包括人类和其他模式生物在内的各种生物体的基因组测序计划完成以来，计算基因组学、系统生物学和遗传学研究的基本目标一直是全面了解基因组中编码的指令集如何在细胞系统和生物体内执行。高通量技术和下一代测序技术的最新进展使研究人员能够收集细胞系统的基因组和其他各个方面的大量数据。这些数据集是理解生物系统遗传控制的详细机制的关键，也是进一步加深我们对细胞生物学知识的关键，具有广泛应用的潜力。该项目将开发基于高维稀疏图形模型的统计机器学习方法，用于基因组数据集的综合分析。由于图形模型提供了一个强大的工具，用于表示作为所观察到的基因组数据基础的未知生物过程的复杂结构，该项目开发的计算方法将能够从基因组规模的数据集中提取关于基因调控系统的遗传控制的丰富信息。该项目还将包括训练下一个-通过指导研究生并将研究成果纳入课程，培养计算生物学家。该项目将涉及计算科学家和生物学家之间的合作，参与高中生的外展计划，为他们提供一个结合生物和计算机科学的替代职业道路。此外，该项目将有助于增加妇女对科学和工程的参与。