Decoding metabolic bottlenecks: Advanced technologies for key gene identification in microbial cell factories.

Advanced gene-identification workflows for microbial cell factories integrate prior-knowledge prediction, phenotype screening, and machine learning to accelerate key-gene discovery and DBTL iteration.

• Why it matters: Productivity bottlenecks persist because biosynthesis is controlled by extensive, complex regulatory networks, making key-gene identification difficult.
• What they did: The review compares prior-knowledge-guided prediction and phenotype-oriented screening, then highlights machine learning for functional annotation and genome-scale metabolic models (GEMs), plus multi-target iterative engineering tools.
• The result: Combining these technologies is proposed to speed the Design-Build-Test-Learn (DBTL) cycle, supporting more rational and efficient engineering of next-generation microbial cell factories.

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