Algebraic and Statistical Models of Redox Signaling

描述(由申请人提供)：维克森林大学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，用于对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，在包括癌症、神经退行性疾病和衰老在内的各种正常和疾病条件下，氧化还原调节网络是细胞信号交流的中心。该项目将1)识别由于氧化还原依赖的信号而在半胱氨酸残基上修饰的一组全面的细胞蛋白质；2)将给定的细胞微扰剂(即氧化剂和抗氧化剂)的浓度与其相关的氧化还原信号相关联；3)将网络与特定的微扰剂相关联；以及4)产生与这些通路相关联的细胞网络的拓扑和动态模型。这些模型将覆盖现有的关于蛋白质/蛋白质相互作用和激酶级联的数据，以产生一个更全面的细胞调控及其生物学结果模型。 一种独特的建模策略将使用计算代数和贝叶斯网络分析来建模这些事件。计算机代数技术将下一状态函数构造为有限域上的多项式。代表基本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存关系。贝叶斯网络分析产生变量之间的概率依赖关系。贝叶斯和计算代数方法的结合将对网络的可靠性和预测氧化剂和抗氧化剂扰动的生物结果的能力产生积极影响。 这样的模型只能用大量和一致的数据集来产生，而共同研究人员开发的新试剂和程序极大地扩展了目前有限的方法，以在大状态、“蛋白质组”的基础上识别氧化还原依赖的信号通路的组成部分。该项目将评估一系列细胞扰动的蛋白质和相关生物终点的氧化修饰，提供以前无法获得的关于氧化还原依赖信号的生物学数据。有了这些试剂和方法以及数学工具的结合，这个研究小组处于独特的地位，可以采用系统生物学的方法，首次对氧化还原信号转导通路进行稳健的建模。该项目的成果--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综合清单--将提供对人类细胞中氧化还原信号网络的系统生物学理解。

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.