Mathematical modeling has been applied to biology as a tool to help understand the complexity of protein interactions through complex networks. We proposed a hybrid mathematical framework that integrates logical modeling and stochastic analysis using the Chemical Master Equation (CME) to study signal transduction in complex biological networks. The logical models describe the network behavior based on regulatory rules, leading to the identification of stable system states. On the other hand, stochastic models such as the CME take into account random molecular fluctuations and describe the system as dynamic state transitions over time. As a case study, we study an interaction network of angiogenesis, the biological process by which new blood vessels form from existing ones. We built a complex interaction network and used it to apply our models. The logical model identifies key attractors associated with angiogenic and non-angiogenic phenotypes, while the stochastic model, implemented using the Gillespie algorithm, captures dynamic fluctuations and transitions between states. This hybrid strategy we propose offers a powerful and generalizable methodology for studying regulatory systems where both network structure and molecular randomness critically influence biological outcomes.