Tensor Computations for Modeling Large-Scale Molecular Biological Data

用于大规模分子生物学数据建模的张量计算

• 批准号: 7925096
• 负责人: Orly Alter
• 金额: $ 17.78万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-19 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7925096
• 关键词: Alcoholism; Algorithms; Biological; Biological Process; Biology; Brain; Cancer Biology; Cell Cycle; Cell Proliferation; Cell physiology; Code; Computer software; DNA; DNA Binding; DNA Microarray Chip; DNA copy number; DNA-Binding Proteins; Data; Data Set; Eukaryota; Eukaryotic Cell; Foundations; Fourier Transform; Future; Genes; Genetic Transcription; Genome; Genomics; Goals; Gravitation; High Performance Computing; Human; Internet; Laboratories; Life; Malignant Neoplasms; Measures; Medicine; Messenger RNA; Meta-Analysis; Modeling; Molecular; Motion; Nature; Neurosciences; Organism; Orthologous Gene; Pathway interactions; Pattern; Pheromone; Physics; Process; Property; Proteins; Proteomics; RNA; Regulation; Replication Initiation; Research; Research Personnel; Scheme; Sequence Alignment; Signal Transduction; Statistical Models; Structure; System; Testing; Time; Tissue Sample; Transcript; Variant; Work; Yeasts; addiction; austin; base; biological systems; comparative; computerized data processing; functional genomics; gel electrophoresis; genome-wide; high throughput technology; insight; mRNA Expression; mathematical model; molecular scale; novel; parallel computing; physical process; platform-independent; predictive modeling; programs; research study; response; success; tool; transcription factor; trend; web site

DESCRIPTION (provided by applicant): Our research is motivated by recent advances in high-throughput technologies, such as DNA microarrays, which make it possible to record the complete genomic signals that guide the progression of cellular processes. Future predictive power and discovery in biology and medicine will come from the mathematical modeling of the rapidly growing number of these large-scale molecular biological datasets. To this end, we built the first data-driven predictive models for genomic data using frameworks from matrix computation. We illustrated thesse models in the analyses of, e.g., cell cycle expression data and transcription factors' and replication initiation proteins' DNA-binding data. The power of our models to predict previously unknown biological principles was demonstrated with a prediction of a novel mechanism of regulation that correlates replication initiation with cell cycle-regulated transcription in yeast. Now, we aim to validate experimentally this computational prediction by collecting and analyzing genome- wide expression under conditions that are thought to decouple replication from cell cycle transcription in eukaryotes. These experiments will test the ability of our mathematical models to correctly predict biological principles. The relation between replication and transcription during the cell cycle will also be illuminated. We also aim to develop the first data-driven predictive tensor computation models for large-scale molecular biological data. The structure of these data is of an order higher than that of a matrix, especially when integrating data from different studies. Flattened into a matrix much of the information in the data is lost. We will study analytically several possible tensor frameworks, and implement algorithms to compute and visualize them. We will apply these mathematical tools to biological data from studies of cancer, cellular proliferation and the cell cycle. This program will result with new insights into the interconnections among the biological programs of cancer, cellular proliferation and the cell cycle. Our goal is to enable better understanding and ultimately also control of life processes on the molecular level. These models may become the foundation of a future in which biological systems are modeled as physical systems are today. The predicted mechanism of regulation may be at the basis of a future where the cell division cycle and cancer can be controlled.

描述（由申请人提供）：我们的研究受到高通量技术（例如 DNA 微阵列）的最新进展的推动，这些技术使得记录指导细胞过程进展的完整基因组信号成为可能。生物学和医学领域未来的预测能力和发现将来自于对数量快速增长的大规模分子生物学数据集的数学建模。为此，我们使用矩阵计算框架为基因组数据构建了第一个数据驱动的预测模型。我们在细胞周期表达数据、转录因子和复制起始蛋白的 DNA 结合数据等分析中阐明了这些模型。我们的模型预测以前未知的生物学原理的能力通过对一种新的调节机制的预测得到了证明，该机制将酵母中的复制起始与细胞周期调节转录相关联。现在，我们的目标是通过在被认为将真核生物中的复制与细胞周期转录脱钩的条件下收集和分析全基因组表达来通过实验验证这种计算预测。这些实验将测试我们的数学模型正确预测生物学原理的能力。细胞周期中复制和转录之间的关系也将得到阐明。我们还旨在为大规模分子生物学数据开发第一个数据驱动的预测张量计算模型。这些数据的结构比矩阵的结构更高，特别是在整合来自不同研究的数据时。扁平化为矩阵后，数据中的大部分信息都会丢失。我们将分析研究几种可能的张量框架，并实现算法来计算和可视化它们。我们将把这些数学工具应用于癌症、细胞增殖和细胞周期研究的生物数据。该计划将对癌症、细胞增殖和细胞周期的生物学程序之间的相互关系产生新的见解。我们的目标是更好地理解并最终在分子水平上控制生命过程。这些模型可能成为未来的基础，其中生物系统可以像今天的物理系统一样进行建模。预测的调节机制可能是未来细胞分裂周期和癌症可以得到控制的基础。