CAREER: Graph Theoretical Inference and Predictive Dynamic Modeling of Signal Transduction Networks

职业：信号传导网络的图论推理和预测动态建模

• 批准号: 0643529
• 负责人: Reka Albert
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643529&HistoricalAwards=false
• 关键词: CAREER; Graph; Theoretical; Inference; Predictive

Current understanding of many biological signal transduction processes consists of knowing key mediators and their positive or negative effects on the process as a whole, yet is increasingly recognized that these mediators function in concert with each other and with other possibly unidentified components. This CAREER award project integrates graph inference, network analysis and dynamic modeling approaches into a general framework for reconstructing and modeling biological signal transduction networks from partial and indirect knowledge. The methodology under development allows predictive modeling of partially characterized signaling networks where traditional frameworks cannot be applied. The graph synthesis process represents indirect relationships as paths and finds the sparsest graph incorporating all experimental evidence via a minimal binary transitive reduction algorithm. New graph measures of redundancy and centrality are developed to incorporate interaction attributes (sign, timing, synergy) and to elucidate the topological constraints on the dynamical repertoire of signal transduction processes. Stochastic Boolean and piece-wise linear approaches are used to formulate predictive dynamic models of signal transduction networks. The methodology is applied to abscisic acid (ABA) induced stomatal closure, light-induced stomatal opening, and ABA inhibition of seed germination, complex plant biology processes that can eminently serve as model systems to better understand animal signaling. A comprehensive interdisciplinary education/outreach plan is implemented which includes but is not restricted to (i) incorporating a simulation component to teaching Solid State Physics, (ii) developing a course in modeling biological systems, and (iii) distilling the developed methods into software modules to be disseminated as bioinformatics services.

目前对许多生物信号转导过程的理解包括了解关键介质及其对整个过程的积极或消极影响，但越来越多地认识到这些介质相互之间以及与其他可能未识别的组分一起发挥作用。这个CAREER奖项目将图推理，网络分析和动态建模方法集成到一个通用框架中，用于从部分和间接知识重建和建模生物信号转导网络。正在开发的方法允许预测建模的部分特征的信令网络，传统的框架不能应用。 图的合成过程表示为路径的间接关系，并通过一个最小的二进制传递约简算法，找到包含所有实验证据的稀疏图。新的图形措施的冗余性和中心性的开发，将相互作用的属性（符号，时间，协同作用），并阐明信号转导过程的动态剧目的拓扑约束。随机布尔和分段线性方法被用来制定预测的信号转导网络的动态模型。该方法适用于脱落酸（阿坝）诱导的气孔关闭，光诱导的气孔开放，阿坝抑制种子萌发，复杂的植物生物学过程，可以突出作为模型系统，以更好地了解动物信号。 一个全面的跨学科教育/推广计划的实施，其中包括但不限于（i）纳入模拟组件教学固态物理，（ii）开发一门课程，在建模生物系统，和（iii）蒸馏开发的方法到软件模块将作为生物信息服务传播。