Advances in erythritol production through synthetic biology and systems metabolic engineering

Synthetic biology has delivered a toolkit for erythritol production, yet a metabolic trade-off persists: strict dependence on pentose phosphate flux and NADPH regeneration pits product synthesis against cell growth and stress adaptation, rendering most engineering interventions unable to break the yield-productivity trade-off. This Review frames erythritol biomanufacturing within a hierarchical constraint cascade, tracing the progression from native strain optimization through synthetic biology-driven pathway rewiring, cofactor balancing, modular design, to AI-integrated design-build-test-learn (DBTL) cycles. Carbon precursor supply sets the flux ceiling, cofactor availability modulates conversion, and scale-dependent heterogeneities in mixing and feedstocks widen the gap between laboratory design and industrial operation. Comparison with other rare sugars (allulose, tagatose) reveals erythritol's unique challenges: deep pathway embedding and high reducing-power demand. While accelerating enzyme engineering, metabolic modelling and process control, current AI applications are most likely to succeed when coupled with mechanism-based, cross-scale models, rather than merely statistical fits. The next breakthrough lies in closing the loop between real-time sensing and adaptive flux regulation. This vision could be realized through integrated biomanufacturing platforms that combine mechanistic modeling, automated DBTL cycles, and cell-free systems where cellular constraints prove limiting. This Review offers a unified, scale-spanning framework for diagnosing systemic bottlenecks in erythritol biosynthesis and outlines principles for intelligent biomanufacturing of sugar alcohols.