Traditional metabolic engineering has largely focused on the direct construction of synthetic metabolic pathways, often overlooking the critical role of regulation. In contrast, natural metabolic pathways are inherently tightly regulated, enabling robust performance in dynamic environments. Dynamic regulation of synthetic metabolic pathways enhances the reliability of cell factories by improving their performance and ensuring greater robustness, scalability, and stability. Therefore, modern approaches to metabolic engineering should embrace genetic circuits that incorporate dynamic regulatory mechanisms. Biosensors, as key components of these circuits, not only enable precise genetic regulation but also provide real-time monitoring and external interfacing capabilities with diverse signal modalities, including electrical and optical systems. By the incorporation of dynamic control mechanisms, synthetic pathways can be rendered more robust to environmental fluctuations during scale-up and more precisely regulated in therapeutic contexts, such as responsive drug delivery. These capabilities are critical to advancing the reliability and applicability of engineered metabolic systems. Furthermore, the potential for the external control of synthetic metabolic processes, guided by advanced algorithms, underscores the growing importance of machine learning and data-driven approaches. This perspective highlights the necessity of integrating regulation into synthetic pathways and leveraging biosensors to drive the next generation of scalable and adaptive metabolic engineering solutions.
Pisani et al. (Fri,) studied this question.