Synthetic promoter design in plants: integration of computational and experimental approaches

Understanding how to engineer transcriptional regulation in plants is key to advancing both fundamental knowledge and practical applications in plant biology. Native gene promoters, while widely used, are constrained by evolutionary pressures that limit their modularity, tunability, and predictability across genetic backgrounds and species. Synthetic promoters, artificial DNA sequences composed of defined cis-regulatory elements (CREs) for recruitment of gene-specific transcription factors (TFs) and general transcriptional machinery, provide a powerful alternative for achieving fine-tuned transcriptional control. This review examines the design and application of synthetic promoters in plants, emphasizing current strategies, ongoing challenges, and avenues for innovation. We cover the structure of plant promoter architecture, including the contributions of core, proximal, and distal regions, and highlight how promoter grammar (i.e., motif identity, motif distance from transcription start site, spacing between motifs, helical phase of TF binding, motif orientation, and combinatorial interactions between motifs) impacts transcriptional activity. We outline how synthetic promoters are designed and validated via high-throughput reporter assays. Applications of synthetic promoters are discussed across functional genomics studies, biosensor creation, logic gate-based genetic circuits, and practical crop engineering, with examples covering constitutively expressing, hormone-responsive, pathogen-inducible, and abiotic stress-responsive promoter designs. We discuss traditional and emerging computational frameworks that enable CRE identification, novel synthetic promoter generation, and prediction of promoter sequence activity <i>in silico</i> to inform the rational design of promoters with predictable performance and spatiotemporal expression. We emphasize the importance of integrating experimental studies and computational approaches through iterative Design-Build-Test-Learn (DBTL) cycles to standardize and optimize frameworks for synthetic promoter development. By combining insights from plant promoter studies with advances in both plant-specific and non-plant synthetic promoter generation and computational modeling, researchers can expand synthetic promoter libraries to enable complex man-driven transcriptional regulation across various plant systems.