Reconstructing sequence-grammar trajectories enables interpretable and tunable cis-regulatory element design

Background Designing cis -regulatory elements (CREs) with cell-type-specific activity is critical for precise gene and cell therapies. Genomic language models (gLMs) have shown potential for CRE generation, but their optimization is often treated as a black box, limiting understanding of how regulatory grammar emerges and how unsuccessful generation trajectories can be corrected. Results We present Guided Optimization of C is -Regulatory Elements (GO-CRE), an interpretable deep learning framework for cell-type-specific CRE generation. GO-CRE is built on HybriDNA, a hybrid Transformer-Mamba2 gLM that supports iterative sequence optimization under practical computational constraints. GO-CRE then runs reinforcement learning (RL) and reconstructs sequence-grammar trajectories using deconvolved k-mer and motif features. The trajectories exhibit three phases - search, commitment, and optimization - each associated with the acquisition of distinct biological features. In HepG2, trajectory analysis identified a low-complexity polyG trap that motivated an updated reinforcement learning policy, and redirected optimization toward higher activity and specificity. In HepG2 and K562, GO-CRE generated diverse, cell-type-specific CREs with compact lineage-associated grammars, including HNF1B-FOXA1-HNF4A programs in HepG2 and GATA/RUNX-associated programs in K562. Lentiviral massively parallel reporter assays validated the cell-type-specific activity of the generated CREs in both cell types, and showed that HepG2 designs had higher average activity than endogenous CREs. Conclusions GO-CRE integrates efficient gLM-based generation, sequence grammar trajectory reconstruction, and biologically guided reward shaping. It links iterative sequence changes to regulatory grammar and feeds interpretable features back into reward design, and thus enables the design of diverse, experimentally validated, cell-type-specific CREs.