From design–build–test–learn cycles to AI-driven digital twins for bioprocess scale-up in the Genesis Mission era

The Genesis Mission is a U.S. initiative to accelerate bioproduction by integrating synthetic biology with the artificial intelligence (AI) ecosystem. However, it also raises caution regarding AI-driven biotechnology. Biomanufacturing requires the coordinated optimization of microbial metabolism and large-scale bioreactor operations. Machine learning (ML), automation, and large language models (LLMs) can streamline integration of literature and real-time data for multiscale optimization and "digital twin" development. But uncertainty in scale-up performance and commercial risk continue to challenge microbial factory deployment because strains optimized through laboratory design-build-test-learn cycles often underperform in stressed industrial bioreactors. Addressing these gaps will require thorough investigation of strain performance under industrial bioreactor conditions, followed by the development of shared AI-ready biosystems databases, integrative AI methods (e.g., transfer learning, reinforcement learning, and Bayesian Optimization), hybrid digital cell modeling, and technoeconomic analysis across the process chain.