Understanding cellular responses to genetic perturbations is essential for deciphering gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. This presentation will introduce a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. Our method can effectively integrate prior biological knowledge and disentangle basal cell states from perturbation-specific salient representations by leveraging gene embeddings derived from large language models. Through comprehensive evaluations on multiple single-cell CRISPR Perturb-seq datasets, our method outperformed state-of-the-art methods in predicting perturbation outcomes, achieving higher prediction accuracy. Notably, it demonstrated robust generalization to unseen target genes and perturbations, and its predictions captured both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including identifying differentially expressed genes and exploring genetic interactions, demonstrating its utility and versatility. This is joint work with Gefei Wang, Tianyu Liu, Jia Zhao, and Youshu Cheng.