Genomic Network Tomography

基因组网络断层扫描

• 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

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，由蛋白质相互作用组成的信号通路将细胞外条件的信息从细胞壁传递到细胞核，导致基因及其蛋白产物的表达发生变化，使细胞能够适应和生存在不同的环境中。生物学家已经发现了某些信号通路的一部分，但目前对完整的信号网络结构的了解还远未完成。由于体内测量的内在困难，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结构的问题，这些实验表明每个信号通路中利用了哪些蛋白质。细胞信号网络是活细胞生长、发育和存活的基础，因此该项目的结果可能会促进人类疾病、生物传感器开发和生物燃料制造等关键领域的知识水平。该项目研究了一种新的细胞信号网络重建技术，该技术基于现有的高通量实验产生的数据，这些数据表明哪些蛋白质被利用在每个信号通路中，但不直接揭示通路的结构/顺序。细胞信号网络和实验数据用混洗马尔可夫过程进行数学建模，这解释了数据没有揭示通路结构/顺序的事实。混洗马尔可夫模型将网络重构问题归结为马尔可夫转移矩阵的推导问题。基于期望最大化和重要性抽样技术，开发了计算高效的推理算法。使用真实和合成生物数据的计算实验，以及数学分析技术，证明了该模型和算法的能力。