MULTIMODAL CELL CONTEXT INSTRUCTION TUNING FOR CONDITIONAL DNA REGULATORY SEQUENCE GENERATION WITH LARGE LANGUAGE MODELS.

ABSTRACT: Designing biologically plausible regulatory sequences, such as enhancers, is a key challenge in synthetic biology. Current approaches either classify active enhancers or generate synthetic sequences independently but often fail to address the need for cell-type-specific recruitment of multiple transcription factors (TFs) for effective transcription activation. This oversight leads to suboptimal designs. To overcome these limitations, we propose Leonine, a novel framework redefining enhancer sequence design as a multimodal question-answering task within a cellular context. Leonine utilizes large language models (LLMs) trained on DNA sequences and integrates multimodal cellular data-including promoter sequences, gene expression profiles, and cell type information-to optimize enhancer sequenc

SUBMITTER: Liu J 

PROVIDER: S-EPMC12718082 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature