Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes is crucial for optimizing bioproduction processes. The quantification of biomass during the induction phase of a recombinant bioprocess presents challenges due to the biological burden caused by protein expression impacting cell morphology and physiology. This variability underscores the importance of accurate biomass estimation in the ever-evolving field of process development. To address this, a method based on first principles was introduced to quantify biomass in real-time without the need for offline sampling or representative training data sets. This soft sensor approach allows for the estimation of biomass and specific growth rate in induced recombinant fed-batch processes, offering a valuable tool for process development and control.
In process development, maximizing the space-time yield of the product while ensuring product quality attributes is a primary goal. Fed-batch processes provide metabolic control over cell metabolism, making the accurate estimation of biomass concentration essential for calculating metabolic variables like specific growth rates and yields. Conventionally, biomass quantification involves time-consuming and operator-dependent gravimetric methods, which may not capture the actual cells involved in the bioreaction. Various sensors using optical or impedance methods offer in-line biomass quantification but may require calibration for different process conditions and strains. Soft sensors based on first principles provide a flexible and generalizable approach to real-time biomass estimation, critical for efficient process development.
The soft sensor approach relies on macroscopic mass balances and elemental constraints to estimate biomass concentration and specific growth rates in real-time. By leveraging elemental balancing principles, the method can detect gross errors such as wrong stoichiometric assumptions or sensor failures automatically. This approach is particularly beneficial in scenarios with variable growth stoichiometry, where traditional methods may struggle to provide accurate estimations. The utilization of first principles and elemental balances ensures the robustness and adaptability of the soft sensor across different process conditions and metabolic states.
Two microbial model systems, Pichia pastoris and E. coli, were employed to validate the real-time estimation approach in induced fed-batch cultures. The method demonstrated its efficacy in estimating biomass concentration and specific growth rates under varying metabolic and physiological conditions. By comparing the soft sensor approach with other methods like Luedeking-Piret-type models and capacitance probes, the superiority of the first principles-based soft sensor for accurate and dynamic biomass estimation was highlighted. The ability to detect and correct for errors in real-time without the need for offline sampling sets this approach apart in the realm of process development and control.
The significance of accurate biomass estimation in real-time extends beyond process optimization to enable rapid decision-making and automation in bioprocesses. By integrating soft sensors based on fundamental principles, bioproduction facilities can enhance their control strategies and accelerate process development timelines. The seamless adaptation of the soft sensor to changing growth stoichiometry and metabolic states underscores its versatility and reliability in modern industrial bioprocesses. The potential for error detection and correction in a dynamic process environment positions the first principles-based soft sensor as a valuable asset in the pursuit of efficient and high-quality bioproduction.
Key Takeaways:
1. Real-time estimation of biomass and growth rate in recombinant fed-batch processes is essential for optimizing bioproduction efficiency and product quality.
2. Soft sensors based on first principles and elemental balancing offer a robust and adaptable approach to biomass quantification in dynamic bioprocesses.
3. The ability to detect errors automatically and provide real-time feedback without offline sampling sets first principles-based soft sensors apart in process development.
4. By leveraging fundamental principles, soft sensors enable rapid decision-making, automation, and control strategies in bioproduction facilities.
5. Accurate biomass estimation in real-time accelerates process development, enhances control strategies, and ensures high-quality bioproduction outcomes.
6. The versatility and reliability of first principles-based soft sensors make them indispensable tools in modern industrial bioprocesses.
Tags: process analytical technology, process development, secretion, bioreactor, automation, sterilization, bioprocess
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