Genomic Network Tomography

基因组网络断层扫描

基本信息

批准号：
0728767
负责人：
Robert Nowak
金额：
$ 12.34万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-15 至 2010-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728767&HistoricalAwards=false
关键词：
Genomic Network Tomography

项目摘要

In living cells, signaling pathways composed of protein-protein interactions communicate information about extracellular conditions from the cell wall to the nucleus, leading to changes in the expression of genes and their protein products that enable the cell to adapt and survive in diverse environments. Biologists have uncovered portions of certain signaling pathways, but the current understanding of full signaling network structures is far from complete. Due to intrinsic difficulties associated with in vivo measurement, this research considers the problem of inferring the structure of a cellular signaling network using data generated by existing high-throughput experiments that indicate which proteins are utilized in each signaling pathway. Cell signaling networks underlie the growth, development and survival of living cells, and therefore the results of this project may advance the state of knowledge in the critical areas of human disease, biosensor development, and biofuel manufacturing.This project investigates a new technique for the reconstruction of cell signaling networks that is based on data generated by existing high-throughput experiments that indicate which proteins are utilized in each signaling pathway, but do not directly reveal the structure/order of the pathways. The cell signaling networks and the experimental data are mathematically modeled by a shuffled Markov process, which accounts for the fact that the data do not reveal the pathway structure/order. The shuffled Markov model reduces the network reconstruction problem to the task of inferring the Markov transition matrix. Computationally efficient inference algorithms, based on expectation-maximization and importance sampling techniques, are developed for this task. Computational experiments using real and synthetic biological data, as well as mathematical analysis techniques, demonstrate the capabilities of the model and algorithms.

在活细胞中，由蛋白质 - 蛋白质相互作用组成的信号通路传达了有关细胞外条件从细胞壁到细胞核的信息，从而导致基因表达及其蛋白质产物的表达变化，从而使细胞在多种环境中适应和生存。生物学家发现了某些信号通路的一部分，但是当前对完整信号网络结构的理解远非完整。由于与体内测量相关的内在困难，本研究考虑了使用现有高通量实验产生的数据来推断细胞信号网络的结构的问题，这些数据表明在每个信号通路中使用哪些蛋白质。 Cell signaling networks underlie the growth, development and survival of living cells, and therefore the results of this project may advance the state of knowledge in the critical areas of human disease, biosensor development, and biofuel manufacturing.This project investigates a new technique for the reconstruction of cell signaling networks that is based on data generated by existing high-throughput experiments that indicate which proteins are utilized in each signaling pathway, but do not directly reveal the structure/order路径。细胞信号网络和实验数据是通过洗牌的马尔可夫过程来建模的，该过程解释了数据未揭示路径结构/顺序的事实。改组的马尔可夫模型将网络重建问题减少到推断马尔可夫过渡矩阵的任务。基于预期最大化和重要性采样技术的计算有效的推理算法是为此任务开发的。使用真实和合成生物学数据以及数学分析技术的计算实验证明了模型和算法的能力。