Real Money Rummy in single-cell biology has shown that even genetically identical cells, in similar growth conditions, display notable levels of heterogeneity. This variability primarily arises from the stochastic nature of intracellular network reactions. To accurately represent this inherent randomness and assess its impact, stochastic models of reaction networks are widely utilized. Nonetheless, integrating these models with actual biological experimental data presents a formidable challenge. This presentation aims to provide a comprehensive overview of the computational methods we have developed for the analysis of stochastic reaction models and their integration with biological data. I will specifically discuss approaches for parametric sensitivity analysis, determining the frequency spectrum of single-cell trajectories, and addressing the Chemical Master Equation (CME) as well as the related stochastic filtering problem.