SynBioGPT2: A dynamic reasoning framework enables high-fidelity design of microbial cell factories

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Computational Biology

Background:

• Traditional Design-Build-Test-Learn cycles are slow due to biological system complexity.
• Large language models (LLMs) struggle with factual consistency and causal reasoning in metabolic engineering.
• Static retrieval-augmented generation (RAG) cannot resolve these LLM limitations.

Purpose of the Study:

• To develop a dynamic reasoning framework, SynBioGPT2, for automated, accurate microbial cell factory design.
• To address factual inconsistencies and multi-hop reasoning deficits in LLMs for systems metabolic engineering.
• To accelerate rational cell factory engineering and biomanufacturing.

Main Methods:

• Integrated paragraph-level hybrid retrieval and an iterative self-evaluation loop.
• Utilized domain-specific expert prompt templates for autonomous knowledge synthesis.
• Employed a dynamic reasoning framework for systems-level biochemical constraint resolution.

Main Results:

• SynBioGPT2 achieved 91.67% accuracy and completeness on a synthetic biology benchmark.
• Successfully resolved systems-level biochemical constraints like redox balancing and allosteric feedback.
• Reconstructed 93.5% of expert-curated metabolic engineering strategies with high fidelity.

Conclusions:

• SynBioGPT2 offers a robust and scalable platform for automated biological discovery.
• The framework mitigates structural reasoning deficits, enabling mechanistically sound cell factory design.
• Accelerates the development of sustainable biomanufacturing through rational cell factory engineering.