Biology has become a data science. The vastness of data acquired in life science require advanced analysis methods and the demand for computation and mathematical modeling is higher than ever before. In this circumstance, I am proposing a new framework in deciphering the information flow within molecular pathways utilizing the financial market analysis skills I acquired during my PhD. The current approach to studying complex molecular pathways in cell biology is to perturb pathway components and then classify the effect as a phenotype. This approach has enabled cell biologist to discover the molecular players of pathways and even to infer hierarchical interaction cascades in pathways. However, under conditions of nonlinearity and high redundancy, the interpretation of these phenotypes can become exceedingly complicated. The proposed research will focus on the following 2 aims. Establish the mechanisms of information flow within the GEF, GTPase network. Establish the mechanisms of detecting the transient topology with protrusion events. The Significance of the proposed research derives from; 1) the project challenges the experimental paradigm of molecular cell biology in that it seeks to circumvent the limitations of molecular perturbation in deciphering pathways with nonlinearity and redundancy; 2) the project introduce to the field of molecular cell biology a theoretical foundation for the definition of causality; and 3) the unique data sets available between the Danuser and Hahn labs will allow me to develop this framework on the example of GEF-GTPase interaction networks, in which the current tools fail to address the redundancy and non-linearity.