Astaxanthin biomanufacturing under hierarchical constraints: Integrating synthetic biology and artificial...

Astaxanthin is a high-value ketocarotenoid of growing industrial importance; however, its scalable biomanufacturing is constrained by inherent biological complexity. Production performance is governed by hierarchical constraints, including molecular instability, pathway-level flux competition, and system-level trade-offs between cellular growth and stress-induced synthesis. These tightly coupled constraints lead to nonlinear responses and persistent optimization plateaus. These challenges cannot be effectively resolved through localized genetic or process interventions.To address this challenge, this review reconceptualizes astaxanthin biosynthesis as a problem of hierarchical constraint management and proposes a hierarchy-aligned, AI-assisted framework. Within this framework, artificial intelligence operates through distinct pathways across biological scales. At the molecular level, protein language models and sequence-structure-aware approaches enable efficient exploration of enzyme sequence space, prioritizing variants that improve catalytic performance and stability. At the pathway level, graph-based learning models capture network topology, flux coupling, and branch competition, allowing identification of distributed metabolic bottlenecks and coordinated intervention targets. At the system level, convolutional neural networks and multimodal learning approaches quantify phenotypic states and integrate multi-omics data to characterize growth-production trade-offs and cellular state transitions. Large language models further function as an integrative layer, linking model outputs, experimental knowledge, and design constraints to support decision-making and hypothesis generation.By explicitly aligning computational abstractions with biological hierarchy, this framework transforms AI from a generic predictive tool into a structured methodology. It links phenotypic observation, pathway-level reasoning, and molecular design. This approach improves the organization of complex design spaces, reduces reliance on empirical trial-and-error, and supports rational, multiscale optimization. The conceptual framework is extendable to other secondary metabolites whose biosynthesis is conditional, resource-intensive, and tightly coupled to cellular physiology.