Precision Microalgae: A New Conceptual Framework for Bioengineering Applications

Microalgae are promising platforms for biomass production, carbon capture, biofuels, and high-value bioproducts. However, despite significant advances in cultivation technologies, reactor engineering, and metabolic engineering, industrial implementation remains limited. This gap suggests that the main challenge of microalgae biotechnology lies not in the availability of productive strains or cultivation systems, but in managing the environmental and physiological heterogeneity that emerges during scale-up. This structured narrative review selected literature using predefined descriptors and relevance-based inclusion criteria and organized the evidence into five thematic domains encompassing cultivation-scale constraints, cellular physiology, bioengineering, precision technologies, and industrial translation. This review examines macrospatial bottlenecks related to light distribution, gas transfer, hydrodynamics, and reactor operation, alongside microspatial constraints involving cell cycle regulation, carbon allocation, metabolic adaptation, and stress responses. Recent advances in adaptive cultivation, real-time monitoring, artificial intelligence, digital twins, computational modeling, and bioengineering are discussed as tools to transform biological and environmental variability into actionable information. Based on concepts established in precision agriculture, this review proposes precision microalgae as a conceptual framework that integrates reactor engineering and cell physiology with three operational pillars: real-time monitoring, predictive modeling, and adaptive control. Its specific contribution is to connect currently fragmented technological and biological advances within a common framework for managing multiscale heterogeneity during cultivation and scale-up. Overall, the available evidence supports the operational logic of this framework, although its generalized effectiveness under industrial conditions remains to be demonstrated.