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

目前对许多生物信号转导过程的了解包括了解关键的介体及其对整个过程的积极或负面影响，但越来越多的人认识到这些介体彼此之间以及与其他可能未知的成分一起发挥作用。这个职业奖项目将图推理、网络分析和动态建模方法集成到一个通用框架中，用于根据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对不能应用传统框架的部分特征的信令网络进行预测性建模。图综合过程将间接关系表示为路径，并通过最小二进制传递约简算法找到包含所有实验证据的最稀疏图。开发了新的冗余度和中心性的图度量，以纳入交互属性(符号、时间、协同)，并阐明了信号转导过程动态谱系的拓扑约束。利用随机布尔法和分段线性法建立了信号转导网络的预测动态模型。该方法被应用于脱落酸(ABA)诱导的气孔关闭、光诱导的气孔开放和ABA对种子萌发的抑制，这些复杂的植物生物学过程可以很好地作为模型系统来更好地理解动物信号。实施了一项全面的跨学科教育/推广计划，其中包括但不限于：(I)在固体物理教学中加入模拟部分，(Ii)开发生物系统建模课程，以及(Iii)将开发的方法提炼成软件模块，作为生物信息学服务进行传播。

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.