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为人类细胞中氧化还原调节事件的网络建模开发理论、算法、计算工具和研究方法。 最近的研究表明，氧化还原调节的网络是在各种正常和疾病条件下细胞信号通信的核心，包括癌症，神经退行性疾病和衰老。该项目将1）鉴定一组全面的细胞蛋白质，这些蛋白质由于氧化还原依赖性信号而在半胱氨酸残基处被修饰; 2）将给定细胞干扰剂的浓度（即，氧化剂和抗氧化剂）及其相关的氧化还原信号; 3）将网络与特定的扰动物相关联;以及4）产生与这些途径相关联的细胞网络的拓扑和动态模型。这些模型将覆盖在现有的蛋白质/蛋白质相互作用和激酶级联反应的数据上，以产生更全面的细胞调控及其生物学结果的模型。 一个独特的建模策略将使用计算代数和贝叶斯网络分析来模拟这些事件。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。 代表潜在生物网络的共识模型将识别蛋白质修饰和生物反应的相互依赖性。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积极影响网络的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。 这样的模型只能用大的和一致的数据集来产生，并且共同研究者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，“蛋白质组学”的基础上识别氧化还原依赖性信号通路的组分。该项目将评估蛋白质的氧化修饰和一组细胞干扰物的相关生物学终点，提供以前无法获得的氧化还原依赖性信号传导的生物学数据。有了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地位，可以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鲁棒建模。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全面列表-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.