CAREER: Bayesian Signal Processing for Uncovering Gene Regulatory Networks

职业：贝叶斯信号处理揭示基因调控网络

• 批准号: 0546345
• 负责人: Yufei Huang
• 金额: $ 40万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-15 至 2011-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546345&HistoricalAwards=false
• 关键词: CAREER; Bayesian; Signal; Processing; Uncovering

The advent of high throughput technologies including DNA sequencing and microarray experiments has reshaped the landscape of molecular and cellular biology; A new field known as computational systems biology (CSB) has emerged to be a central discipline. This research investigates Bayesian signal processing solutions that enable uncovering gene regulatory networks (GRNs), the regulatory systems linking proteins and targets. Uncovering GRNs will help elucidate biological system structure, system dynamics, control mechanisms, and the design principles, thereby providing clues for new therapeutics strategies. At the heart of the GRNs research are system modeling, inference, and data integration, all of which are the key issues of Bayesian signal processing. This research aims to: (1) investigate a novel graphical modeling framework for accurate and robust modeling of gene regulation; (2) investigate a novel Bayesian framework for systematic and efficient data integration; (3) investigate systematic procedures for effective results validation and error analysis; (4) apply the developed solutions to uncover the cell cycle GRNs of yeast and malaria parasite. This research is motivated by the inadequacy of current research in modeling, data integration, and results validation, and the shortfall in the practices that are inharmonious with the iterative nature of CSB. This research is anticipated to culminate in the formulation of powerful modeling and data integration frameworks that will significantly improve the accuracy and robustness of the uncovered GRNs, and under which further improvements and extensions to systems beyond genes can be systematically carried out. The research is also integrated with signal processing curriculum advance research training, and education of students at all levels. It promotes diversity and outreach in the area of CSB.

包括DNA测序和微阵列实验在内的高通量技术的出现重塑了分子和细胞生物学的格局;被称为计算系统生物学（CSB）的新领域已经成为一门中心学科。本研究调查了贝叶斯信号处理解决方案，该解决方案能够揭示基因调控网络（GRNs），即连接蛋白质和靶标的调控系统。揭示GRNs将有助于阐明生物系统结构、系统动力学、控制机制和设计原则，从而为新的治疗策略提供线索。 广义随机数研究的核心是系统建模、推理和数据集成，所有这些都是贝叶斯信号处理的关键问题。本研究旨在：（1）研究一种新的图形建模框架，用于精确和鲁棒的基因调控建模;（2）研究一种新的贝叶斯框架，用于系统和有效的数据集成;（3）研究有效结果验证和错误分析的系统程序;（4）应用开发的解决方案来揭示酵母和疟原虫的细胞周期GRNs。本研究的动机是目前的研究在建模，数据集成和结果验证的不足，并在实践中的不足，是不和谐的CSB的迭代性质。这项研究预计将最终制定强大的建模和数据集成框架，这将显着提高未被发现的GRNs的准确性和鲁棒性，并在此基础上进一步改进和扩展到基因以外的系统可以系统地进行。该研究还与信号处理课程的高级研究培训和各级学生的教育相结合。 它促进社区支助服务领域的多样性和外联。