RiboRep: Replicate-Aware Cross-Modal Transformers for Codon-Resolved Ribosome Density Prediction

Ribosome profiling enables genome-wide measurement of translation at nucleotide resolution and provides a dynamic view of cellular protein synthesis under diverse biological conditions. Existing computational approaches primarily operate on codon-level representations, potentially losing fine-grained translational signals critical for modeling context-dependent cellular responses. Such predictive translational modeling is increasingly important for emerging biological digital twins, where accurate simulation of molecular-state dynamics is required to characterize cellular adaptation, perturbation response, and phenotype progression. We present RiboRep, a replicate-aware cross-modal transformer for codon-resolved ribosome density prediction. RiboRep jointly models nucleotide-resolution RNA sequences and reference ribosome occupancy signals using dual-stream convolutional encoders, RoPE based self-attention, asymmetric cross-attention, and replicate aware conditioning tokens. Byexplicitly modeling replicate-specific variation and integrating sequence context with experimentally observed translational activity, RiboRep provides a framework for reconstructing and simulating translational states across biological conditions. Across bacterial, yeast, and plant ribosome profiling datasets, RiboRep achieves competitive or improved performance compared with existing baselines, with particularly strong gains on replicate-rich plant datasets. Ablation studies further demonstrate the importance of local codon-aware feature extraction, replicate aware conditioning, and gated readout. Beyond predictive performance, the proposed framework establishes a foundation for translation-aware molecular digital twins capable of modeling ribosome occupancy landscapes, perturbation-induced translational responses, and condition-specific regulatory programs at codon resolution.