The rational design of microbial cell factories is essential for sustainable biomanufacturing, yet the traditional Design-Build-Test-Learn (DBTL) cycle is bottlenecked by the highly non-linear and interconnected nature of biological systems. Although large language models (LLMs) offer computational advantages for automated design, their application in systems metabolic engineering is hindered by factual inconsistencies and a limited capacity for multi-hop causal reasoning—challenges that static single-pass retrieval-augmented generation (RAG) fails to resolve. Here, we present SynBioGPT2, a dynamic reasoning framework that integrates paragraph-level hybrid retrieval, an iterative self-evaluation loop, and domain-specific expert prompt templates to enable autonomous, multi-source knowledge synthesis. Evaluated on a multidimensional synthetic biology benchmark, the architecture achieved 91.67% accuracy and completeness, significantly outperforming zero-shot LLMs and static RAG baselines. We demonstrated the framework's capability to resolve systems-level biochemical constraints, including redox balancing and complex allosteric feedback networks, during the rational computational design of Corynebacterium glutamicum. Furthermore, SynBioGPT2 ensured the high-fidelity extraction of quantitative parameters and successfully reconstructed 93.5% (86/92) of expert-curated metabolic engineering strategies across diverse target products. By mitigating structural reasoning deficits and integrating expert-guided deductive logic, SynBioGPT2 provides a mechanistically robust, scalable platform to accelerate automated biological discovery and rational cell factory engineering.

With the rapid advancements in sustainable development and green chemistry, biotransformation has become increasingly pivotal in the synthesis of bulk chemicals and high-value products. Because natural evolution predominantly favors cellular survival, many valuable compounds, such as 2,4-dihydroxybutanoic acid and 1,2-butanediol, lack corresponding biosynthetic pathways in nature. This limitation calls for the development of fully nonnatural metabolic pathways. By enabling modular design and incorporating novel reactions, such pathways allow efficient de novo synthesis of compounds without known natural biosynthetic pathways. Nonetheless, their implementation may introduce new challenges, such as increased metabolic burden and the accumulation of toxic intermediates. Expanding the scope and efficiency of biotransformation through rational nonnatural pathways has become a key challenge. To address this, researchers have developed various computational methods for nonnatural pathway design, and two major types of methods, template-based and template-free methods, are reviewed here. We evaluate their practical applications in guiding the construction of microbial cell factories and analyze their effectiveness. Additionally, we compiled 55 experimentally validated nonnatural pathways from recent literature to establish a dataset for evaluating the strengths and limitations of these pathway design methods. By simulating a wide range of experimentally verified pathways, we highlight the gaps between computational predictions and empirical feasibility. Finally, we propose potential strategies to bridge these gaps, offering theoretical insights and practical guidance for integrating computational tools with experimental synthetic biology.