Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RL

• URL: http://arxiv.org/abs/2505.20578v1
• Date: Mon, 26 May 2025 23:27:50 GMT
• Title: Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RL
• Authors: Xingyu Chen, Shihao Ma, Runsheng Lin, Jiecong Lin, Bo Wang,
• Abstract summary: Ctrl-DNA is a novel constrained reinforcement learning framework tailored for designing regulatory DNA sequences with controllable cell-type specificity.<n>Our evaluation on human promoters and enhancers demonstrates that Ctrl-DNA consistently outperforms existing generative and RL-based approaches.
• Score: 17.05124539734196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing regulatory DNA sequences that achieve precise cell-type-specific gene expression is crucial for advancements in synthetic biology, gene therapy and precision medicine. Although transformer-based language models (LMs) can effectively capture patterns in regulatory DNA, their generative approaches often struggle to produce novel sequences with reliable cell-specific activity. Here, we introduce Ctrl-DNA, a novel constrained reinforcement learning (RL) framework tailored for designing regulatory DNA sequences with controllable cell-type specificity. By formulating regulatory sequence design as a biologically informed constrained optimization problem, we apply RL to autoregressive genomic LMs, enabling the models to iteratively refine sequences that maximize regulatory activity in targeted cell types while constraining off-target effects. Our evaluation on human promoters and enhancers demonstrates that Ctrl-DNA consistently outperforms existing generative and RL-based approaches, generating high-fitness regulatory sequences and achieving state-of-the-art cell-type specificity. Moreover, Ctrl-DNA-generated sequences capture key cell-type-specific transcription factor binding sites (TFBS), short DNA motifs recognized by regulatory proteins that control gene expression, demonstrating the biological plausibility of the generated sequences.

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