ScholarWorks@중앙대학교

초록

Microbial production of value-added chemicals is a sustainable and environmentally friendly alternative to conventional chemical synthesis. However, in dynamic fermentation environments, static control strategies like promoter engineering and constitutive gene expression often fail to balance growth and production. The productivity and stability of engineered microbes are limited by an intrinsic conflict between maximising metabolic flux for product formation and maintaining cellular fitness. Static metabolic engineering strategies, which often create growth–production trade-offs, cannot adapt to dynamic physiological changes from nutrient, byproduct, and stress response fluctuations. Although external environmental controls can partially alleviate this imbalance, they are labour-intensive and difficult to scale. Advances in autonomous genetic regulation have developed self-adaptive systems through which microbes can sense and respond to intracellular and environmental cues in real time. These dynamic circuits maintain homeostasis while optimising metabolic flux by integrating sensing, feedback, and control modules. This review summarises recent progress in autonomous single-cell and population-level regulation strategies for microbial cell factories. Single-cell strategies encompass metabolite- and cell burden-responsive systems that dynamically rebalance flux and mitigate stress in individual cells. Population-level strategies include quorum-sensing-responsive and pH-responsive systems that coordinate collective behaviour and environmental adaptation. Feedback and feedforward control architectures are highlighted in each category to illustrate distinct mechanisms of achieving stability, responsiveness, and predictive adaptation. Underlying design principles, representative applications, and future perspectives for the construction of robust and intelligent microbial production systems are also discussed. The development of next-generation cell factories capable of autonomously optimising performance in fluctuating bioprocessing conditions can be accelerated by integrating systems biology, synthetic biology, and control theory.

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