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We study how randomness in gene expression influences cellular dynamics and decision-making. Our work constructs biologically detailed stochastic models of gene regulation and derives novel exact or approximate analytical solutions, which we integrate with statistical inference and AI-based methods to analyse single-cell transcriptomics data. By linking theory directly to experiments, we extract quantitative biological insight, disentangle biological and technical noise, and reveal how variability shapes cellular function.