Genetic regulatory networks, or GRNs, are made up of genes, proteins, and regulatory factors that control how cells work.  Most of the time, traditional fixed models cannot explain how gene expression is naturally random because of changes in molecules and noise in the surroundings. Stochastic differential equations (SDEs) are a strong way to model this kind of variation, which lets us give more accurate descriptions of network stability, regulatory delays, and transcriptional bursts. SDE-based methods help us understand genetic diversity, stability, and cellular decision-making better by adding noise to dynamic models.