Challenges and Opportunities in Smart Biosensing for Biomanufacturing.

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Genetic Circuits

Background:

• Traditional metabolic engineering often neglects pathway regulation, unlike natural systems.
• Natural metabolic pathways exhibit inherent tight regulation for robust performance in dynamic environments.

Purpose of the Study:

• To advocate for the integration of dynamic regulatory mechanisms into synthetic metabolic pathways.
• To highlight the role of biosensors in enabling precise genetic regulation and real-time monitoring.

Main Methods:

• Incorporating genetic circuits with dynamic regulatory mechanisms.
• Utilizing biosensors for precise genetic control and interfacing with external systems.
• Leveraging advanced algorithms and machine learning for external control of metabolic processes.

Main Results:

• Dynamic regulation enhances the reliability, robustness, scalability, and stability of cell factories.
• Biosensors provide real-time monitoring and enable interfacing with electrical and optical systems.
• Synthetic pathways become more robust to environmental fluctuations and precisely regulated for applications like drug delivery.

Conclusions:

• Integrating regulation and biosensors is crucial for advancing the reliability and applicability of engineered metabolic systems.
• Dynamic control mechanisms are essential for scalable and adaptive metabolic engineering solutions.
• Machine learning and data-driven approaches will play a growing role in external control of synthetic metabolic processes.