Yeast surface display (YSD) is an important technological platform integrating protein engineering, synthetic biology, and industrial biocatalysis. Its catalytic efficiency and stability highly depend on the dynamic synergy of the three-tier microenvironments: the cell wall, intracellular environment, and extracellular compartments. This review systematically elaborates on the core components of the YSD microenvironment, including the cell wall, anchor proteins, intracellular secretion pathways, and extracellular physicochemical properties, and clarifies their critical roles in the folding, activity, and anchoring efficiency of displayed proteins. In addition, this review summarizes targeted regulation strategies, including cell wall gene engineering, optimization of anchor proteins and linkers, enhancement of intracellular secretion pathways, adaptation of extracellular culture conditions, and spatial arrangement of multienzyme co-display. It reveals that synergistic optimization of the three-tier microenvironments can significantly improve display efficiency and catalytic performance. YSD technology based on microenvironment regulation has achieved substantial application progress in industrial production, biomedicine, and environmental remediation. However, current microenvironment regulation still faces challenges in terms of precision, compatibility, and stability. Future research should focus on developing corresponding regulation strategies to break through technical bottlenecks and promote the industrial application of YSD. Microenvironment regulation provides important theoretical and technical support for the performance optimization and industrial translation of YSD.

Ethylene, a fundamental feedstock in the petrochemical industry, is conventionally produced via energy-intensive steam cracking of fossil fuels, a process associated with high energy consumption and carbon emissions. Constructing photosynthetic cyanobacterial cell factories for the direct, solar-driven conversion of CO₂ to ethylene offers a promising route for the green production of ethylene. This review systematically summarizes recent advances in this field, highlighting the core catalytic mechanism of the ethylene-forming enzyme (EFE), the screening and optimization of key genetic elements, metabolic engineering strategies (including gene copy number amplification, precursor supply enhancement, rewiring of carbon flux, and alleviation of by-product inhibition), and the optimization of cultivation processes. Furthermore, we identify the key challenges currently impeding cyanobacterial production of ethylene, such as low yields and efficiencies, insufficient genetic stability, and bottlenecks in large-scale cultivation. Finally, we outline future development directions—advancing synthetic biology tools, enabling precise regulation of metabolic networks, designing efficient photobioreactors, and integrating industrial technologies, with the aim of providing a theoretical foundation for establishing robust and high-performance cyanobacterial cell factories for ethylene production and facilitating their practical application.

Microbial fermentation technology is crucial in modern biomanufacturing processes, such as biopharmaceutical production. However, traditional fermentation process control faces bottlenecks including offline monitoring delay, reliance on empirical experience, and a lack of precise and real-time regulation. Machine learning (ML), with its powerful data extraction and predictive modeling capabilities, has emerged as a key tool for driving the transition from traditional fermentation processes to intelligent paradigms. This paper systematically reviewed the latest research progress on the closed-loop intelligent fermentation control systems, spanning from underlying data perception to high-level decision-making. First, the specific applications of ML in fermentation optimization were elaborated, with a focus on systematic modeling workflows in response to the inherent “black-box” nature and data-lag limitations of purely data-driven models. Second, by comparing the monitoring characteristics of various process analytical technologies, the critical supporting role of Raman spectroscopy in real-time fermentation monitoring was elucidated, and cutting-edge approaches employing semi-supervised learning and data augmentation strategies to address the scarcity of high-quality labeled samples were summarized. Furthermore, the ML-driven “intelligent perception-feedback closed-loop” control mechanism was thoroughly analyzed, and the transformation of fermentation control from traditional “macroscopic feeding regulation” to “microscopic metabolic precise guidance” was expounded. Finally, the future development trajectories for intelligent fermentation was envisioned from two perspectives, namely the construction of cross-genus universal predictive models and the application of digital twin technology. This review aims to provide valuable technical references for the intelligent upgrading of the biomanufacturing sector and the efficient development of fermentation processes.