Pisani Mara, Carbonell Pablo
Synthetic and Systems Biology Lab for Biomedicine, Instituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, 80125 Naples, Italy.
Open University Affiliated Centre, Milton Keynes MK9 1FW, U.K.
ACS Synth Biol. 2025 Sep 19;14(9):3267-3274. doi: 10.1021/acssynbio.5c00203. Epub 2025 Aug 22.
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.
传统的代谢工程主要侧重于直接构建合成代谢途径,常常忽视调控的关键作用。相比之下,天然代谢途径本身受到严格调控,能够在动态环境中稳健运行。合成代谢途径的动态调控通过提高细胞工厂的性能并确保更高的稳健性、可扩展性和稳定性,增强了其可靠性。因此,现代代谢工程方法应采用包含动态调控机制的遗传回路。生物传感器作为这些回路的关键组件,不仅能实现精确的基因调控,还具备实时监测以及与包括电气和光学系统在内的多种信号模式进行外部连接的能力。通过引入动态控制机制,合成途径在放大过程中对环境波动的耐受性更强,在治疗环境(如响应式药物递送)中能得到更精确的调控。这些能力对于提高工程化代谢系统的可靠性和适用性至关重要。此外,在先进算法的指导下对合成代谢过程进行外部控制的潜力,凸显了机器学习和数据驱动方法日益增长的重要性。这一观点强调了将调控整合到合成途径中并利用生物传感器来推动下一代可扩展和适应性强的代谢工程解决方案的必要性。