10 research directions for senior fermentation engineers
The core logic of these directions is to achieve global optimization of the fermentation process from cell growth to product synthesis through closed-loop control driven by metabolic demands, stage adapted resource allocation, and multidimensional interference suppression.
1. Closed loop regulation based on dynamic parameters: Based on real-time monitoring of metabolic parameters (such as dissolved oxygen, pH, substrate concentration), the stirring, aeration, or feeding rates are dynamically adjusted through feedback algorithms to maintain the steady state of bacterial metabolism.
2. Optimization of staged feeding strategy: Supplement carbon and nitrogen sources in stages based on microbial growth curves (lag period, logarithmic period, stable period) to balance resource allocation for bacterial growth and product synthesis.
3. Gradient optimization based on dissolved oxygen: By adjusting the stirring power and ventilation rate, the dissolved oxygen concentration gradient is controlled to match the oxygen requirements of different metabolic stages (such as the need for low dissolved oxygen in the later stages of antibiotic fermentation).
4. Enzyme activity control based on temperature coupling: Adjust the temperature in stages according to the kinetic characteristics of key enzymes synthesized from the target product (such as prolonging enzyme stability at low temperatures and accelerating cell growth at high temperatures).
5. Research on pH metabolic equilibrium: Maintain pH within the optimal range of enzyme activity and membrane permeability through automatic acid-base supplementation, avoiding feedback inhibition of metabolic byproducts (such as organic acids).
6. Targeted control based on metabolic diversion: using nutritional restrictions (such as phosphate starvation) or inducers (such as IPTG) to close competitive metabolic pathways and enhance target product flux.
7. Digestion mechanism based on foam synergy: combine mechanical defoamer (physical defoamer) and silicone defoamer (chemical foam inhibitor) to reduce the impact of foam on gas mass transfer and bacterial infection probability.
8. Maintenance of genetic robustness of bacterial strains: By dynamically adjusting selection pressure (such as antibiotic concentration gradients) or plasmid stability elements, gene loss or mutation can be prevented during continuous passage.
9. Multi source sensor data fusion: Integrating online Raman spectroscopy, exhaust gas mass spectrometry, and electrochemical sensor data to construct a metabolic flux model for cross scale accurate prediction.
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10. Aseptic boundary system design: Adopting a two-stage air filter (0.2 μ m), in place sterilization (SIP), and positive pressure tank control, multiple barriers are established to suppress the risk of bacterial contamination.
