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

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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.