Statistical and Computational Approaches for Integrated Genomics and Proteomics Analysis and Their Applications to Modeling G1/S Transition During Yeast Cell Cycle

整合基因组学和蛋白质组学分析的统计和计算方法及其在酵母细胞周期 G1/S 转变建模中的应用

• 批准号: 0241160
• 负责人: Hongyu Zhao
• 金额: $ 120万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241160&HistoricalAwards=false
• 关键词: Statistical; Computational; Approaches; Integrated; Genomics

Advances in technologies are changing the field of biology to move beyond genomes to transcriptomes, proteomes and metabolomes. It has become clear that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering. Although the importance of integrating various types of biological data to address scientific questions is well recognized and appreciated, the potential information carried in different types of data may not be fully realized without a sound and comprehensive statistical framework to integrate these data. In addition, close collaborations among statisticians, biologists, bioinformaticians, and computer scientists are essential to ensure that these statistical methods provide a reasonable description of the biological processes studied and the validity of these methods should be rigorously tested through biological experiments. In this project, a team of researchers with expertise in statistics, genomics and proteomics, bioinformatics, and computer science will develop an integrated approach to reconstructing biological pathways. Statistical and computational methods will be developed to better identify transcription factor targets, to integrate yeast two-hybrid data, protein complex data, protein localization data, and gene expression data to infer protein interaction networks, and to further integrate DNA- protein binding data to reconstruct transcriptional regulatory networks. This project focuses on the G1/S transition during the yeast cell cycle to statistically model and experimentally validate inferred regulatory networks. In addition, parallel computing methods will be developed to overcome the computing bottleneck in the analysis of large-scale networks. The resources generated from this project, both computer programs and network information will be made available to the scientific community. It is anticipated that this project will lead to a statistical framework that can be utilized to dissect biological pathways and also will lead to an approach to integrating expertise from diverse disciplines to address important scientific problems in the post-genome era.With recent progresses in biotechnologies, it has become reality to collect tens of thousands of gene expression and protein expression levels in humans and other organisms. In addition, scientists now are able to monitor interactions among proteins and interactions between proteins and DNA sequences, to investigate the location that each gene is expressed, and to study the overall effects on the whole organism of individual genes through large collections of mutation strains. The availability of such data has led to a revolution in biological and biomedical sciences. Although there is a great potential and an enormous amount of information in these data, the major challenge is how to best integrate, analyze, and interpret these data to understand biological pathways. In this project, statistical and computational methods will be developed to integrate various types of data in an effort to reconstruct biological pathways with a focus on the understanding of gene regulations in cell cycle. The statistical models to be developed will be validated with biological experiments. Computer programs will be developed and distributed to the scientific community after extensive testing to allow biologists and medical researchers to use these tools to study other biological pathways. This project will also develop high-performance computing approaches to implementing the developed methods and will involve training activities in the general area of computational biology and bioinformatics. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.

技术的进步正在改变生物学领域，使其超越基因组，进入转录组、蛋白质组和代谢组。 很明显，需要将预测建模与系统的实验验证相结合，以更深入地了解生物体，治疗靶向和生物工程。 虽然人们充分认识到和理解整合各种类型的生物数据以解决科学问题的重要性，但如果没有一个健全和全面的统计框架来整合这些数据，就可能无法充分实现不同类型的数据所载的潜在信息。 此外，统计学家，生物学家，生物信息学家和计算机科学家之间的密切合作是必不可少的，以确保这些统计方法提供了一个合理的描述所研究的生物过程，这些方法的有效性应通过生物实验进行严格的测试。 在这个项目中，一个具有统计学，基因组学和蛋白质组学，生物信息学和计算机科学专业知识的研究人员团队将开发一种重建生物途径的综合方法。 将开发统计和计算方法以更好地鉴定转录因子靶标，整合酵母双杂交数据、蛋白质复合物数据、蛋白质定位数据和基因表达数据以推断蛋白质相互作用网络，并进一步整合DNA-蛋白质结合数据以重建转录调控网络。 该项目的重点是在酵母细胞周期中的G1/S转换，以统计建模和实验验证推断的调控网络。 此外，将开发并行计算方法，以克服大规模网络分析中的计算瓶颈。 该项目产生的资源，包括计算机程序和网络信息，将提供给科学界。 预计这一项目将导致一个统计框架，可用于剖析生物途径，也将导致一种方法，整合不同学科的专门知识，以解决后基因组时代的重要科学问题，随着生物技术的最新进展，收集人类和其他生物体中数以万计的基因表达和蛋白质表达水平已成为现实。 此外，科学家现在能够监测蛋白质之间的相互作用以及蛋白质和DNA序列之间的相互作用，调查每个基因表达的位置，并通过大量的突变菌株来研究单个基因对整个生物体的整体影响。 这些数据的提供导致了生物和生物医学科学的革命。 虽然这些数据中有巨大的潜力和大量的信息，但主要的挑战是如何最好地整合，分析和解释这些数据以了解生物学途径。 本项目将开发统计和计算方法，以整合各种类型的数据，努力重建生物学途径，重点是了解细胞周期中的基因调控。 将通过生物实验验证拟开发的统计模型。 计算机程序将在广泛测试后开发并分发给科学界，以允许生物学家和医学研究人员使用这些工具研究其他生物途径。 该项目还将开发高性能计算方法，以实施所开发的方法，并将涉及计算生物学和生物信息学一般领域的培训活动。这项赠款是根据联合DMS/NIGMS倡议，以支持研究赠款在数学生物学领域。这是一项由美国国家科学基金会数学科学部（DMS）和美国国立卫生研究院国家普通医学科学研究所（NIGMS）赞助的联合竞赛。