Algebraic and Statistical Models of Redox Signaling

氧化还原信号的代数和统计模型

• 批准号: 7404490
• 负责人: JACQUELYN Su FETROW
• 金额: $ 24.68万
• 依托单位: WAKE FOREST UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7404490
• 关键词: Aging; Algorithms; Antioxidants; Biological; Cell model; Cells; Communication; Computers; Condition; Consensus; Cysteine; Data; Data Set; Dependency; Disease; End Point; Event; Human; Interdisciplinary Study; Malignant Neoplasms; Methods; Modeling; Neurodegenerative Disorders; Outcome; Oxidants; Oxidation-Reduction; Pathway Analysis; Pathway interactions; Phosphotransferases; Positioning Attribute; Post-Translational Protein Processing; Procedures; Proteins; Proteomics; Reagent; Research; Research Methodology; Research Personnel; Signal Pathway; Signal Transduction; Signal Transduction Pathway; Statistical Models; Systems Biology; Techniques; Time; Universities; base; cell growth regulation; computer based statistical methods; computerized tools; forest; network models; protein protein interaction; response; theories; tool

DESCRIPTION (provided by applicant): An interdisciplinary research group at Wake Forest University aims to develop theory, algorithms, computational tools, and research methodologies for network modeling of redox-regulated events in human cells. Recent research indicates that redox-regulated networks are central to the communication of cellular signals under a variety of normal and disease conditions, including cancer, neurodegenerative diseases, and aging. This project will 1) identify a comprehensive set of cellular proteins modified at cysteine residues as a result of redox-dependent signaling; 2) correlate the concentration of a given cellular perturbant (i.e., oxidant and anti-oxidant) and its associated redox signal; 3) associate networks with particular perturbants; and 4) produce both topological and dynamic models of the cellular network associated with these pathways. These models will be overlaid on existing data on protein/protein interactions and kinase cascades to produce a more comprehensive model of cellular regulation and its biological outcomes. A unique modeling strategy will use computational algebra and Bayesian network analysis to model these events. The computer algebra techniques construct next-state functions as polynomials over a finite field. Consensus models that represent the underlying biological network will identify interdependencies of the protein modifications and biological responses. Bayesian network analysis produces probabilistic dependencies among the variables. The combination of Bayesian and computational algebra approaches will positively impact the network reliability and ability to predict the biological outcomes of oxidant and anti-oxidant perturbations. Such models can only be produced with large and consistent data sets, and the new reagents and procedures developed by the co-investigators greatly extend the currently limited methods to identify the components of redox-dependent signaling pathways on a large-state, "proteomic" basis. This project will assess oxidative modifications of proteins and associated biological endpoints for a set of cellular perturbants, providing previously unattainable biological data on redox-dependent signaling. With these reagents and methods and the combination of mathematical tools, this research group is in a unique position to undertake a systems biology approach and robustly model redox signal transduction pathways for the first time. The project's outcomes - a comprehensive list of the components of redox signaling pathways, their biological consequences, and topological and dynamic network models - will provide a systems biology understanding of redox signaling networks in human cells.

描述（由申请人提供）：Wake Forest University的一个跨学科研究小组旨在开发理论，算法，计算工具和研究方法，用于人类细胞中氧化还原调节事件的网络建模。 最近的研究表明，氧化还原调节的网络对于在各种正常和疾病状况（包括癌症，神经退行性疾病和衰老）下的细胞信号通信至关重要。该项目将1）确定由于氧化还原依赖性信号的导致在半胱氨酸残基修饰的一组全面的细胞蛋白； 2）将给定细胞扰动剂（即氧化剂和抗氧化剂）及其相关的氧化还原信号的浓度相关联； 3）将网络与特定的扰动物相关联；和4）产生与这些途径相关的细胞网络的拓扑模型和动态模型。这些模型将被覆盖在有关蛋白质/蛋白质相互作用和激酶级联反应的现有数据上，以产生更全面的细胞调节模型及其生物学结果。 独特的建模策略将使用计算代数和贝叶斯网络分析来对这些事件进行建模。计算机代数技术在有限场上构建了下一州的多项式功能。 代表潜在生物网络的共识模型将确定蛋白质修饰和生物反应的相互依赖性。贝叶斯网络分析在变量之间产生概率依赖性。贝叶斯和计算代数方法的结合将对网络可靠性和预测氧化剂和抗氧化剂扰动的生物学结果的能力产生积极影响。 这样的模型只能使用大型且一致的数据集产生，并且共同投资者开发的新试剂和程序大大扩展了当前有限的方法，以识别大型氧化还原信号通路的组成部分，“蛋白质组学”基础。该项目将评估一组细胞扰动剂的蛋白质和相关生物学终点的氧化修饰，从而为依赖氧化还原依赖性信号传导提供了以前无法实现的生物学数据。借助这些试剂和方法以及数学工具的组合，该研究小组处于独特的位置，可以首次采用系统生物学方法，并首次对氧化还原信号转导途径进行了牢固建模。该项目的结果 - 氧化还原信号通路的组成部分，其生物学后果以及拓扑和动态网络模型的全面列表，将为人类细胞中的氧化还原信号网络提供系统的生物学理解。

项目成果

Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates.

• DOI: 10.1186/1471-2172-11-41
• 发表时间: 2010-08-03
• 期刊: BMC immunology
• 影响因子: 3
• 作者: Olex AL;Hiltbold EM;Leng X;Fetrow JS
• 通讯作者: Fetrow JS

Comparison of Co-Temporal Modeling Algorithms on Sparse Experimental Time Series Data Sets.

• DOI: 10.1109/bibe.2010.21
• 发表时间: 2010-05
• 期刊: Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering
• 作者: Allen EE;Norris JL;John DJ;Thomas SJ;Turkett WH Jr;Fetrow JS
• 通讯作者: Fetrow JS

Continuous cotemporal probabilistic modeling of systems biology networks from sparse data.

• DOI: 10.1109/tcbb.2010.95
• 发表时间: 2011-09
• 期刊: IEEE/ACM transactions on computational biology and bioinformatics
• 作者: John DJ;Fetrow JS;Norris JL
• 通讯作者: Norris JL