Quantitative analysis of signaling networks necessitates modeling frameworks that explicitly account for both the intrinsic randomnessRandomness of biochemical reactions and the pervasive uncertaintyUncertainty associated with incompletely characterized kinetic parameters. In this chapter, a comprehensive hierarchical Petri net modelPetri nethierarchical of the p16-mediated signaling pathway in higher eukaryotes is developed and examined under deterministic, purely stochastic, and fuzzy stochastic modeling paradigmsParadigmdeterministicParadigmpurely stochasticParadigmfuzzy stochastic. The proposed framework integrates hierarchical Petri nets with stochastic firing semantics and incorporates parameter uncertainty through fuzzy kinetic ratesFuzzy kinetic rates represented by \(\alpha \) -cut decompositionsAlpha-cut decomposition. Simulation experiments are conducted, and the resulting system behaviors are evaluated using non-parametric statistical testsTeststatistical to quantify similarities and discrepancies among the different modeling approaches. The results indicate that, for regulatory systems characterized by sparse molecular populations and incomplete kinetic knowledge, fuzzy stochastic modeling provides a more biologically consistent and informative description by simultaneously addressing randomness and parameter uncertainty.