Modeling the Glucose Concentration for the Recombinant E.coli Bioprocesses

This article presents the sophisticated-to-date carbon mass balance for fed-batch E.coli bioprocesses. The model originates from the distribution of carbon mass from glucose in biomass, off-gas, and hypothetical solutes. The suggested model complements Pirt's equation as a particular case scenario. The approach uses the linear relationship between biomass carbon content per carbon grams in glucose and average cell population age. The carbon balance brings two potential practical benefits. First, it has the potential to assess the type of cell metabolism pathway and to have a soft sensor for the concentration of dissolved products such as acetates. The measure of glucose concentration suggests another finding, assuring the reliance on off-gas information only. The paper introduces an average carbon content ratio in biomass and off-gas, with numerical values of 0.5 in growth-limiting experiments and 0.27 in nonlimiting ones, which may serve as a decision-making criterion for metabolic pathway detection in the future

Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate

In this study, an automatic control system is developed for the setpoint control of the cell biomass specific growth rate (SGR) in fed-batch cultivation processes. The feedback signal in the control system is obtained from the oxygen uptake rate (OUR) measurement-based SGR estimator. The OUR online measurements adapt the system controller to time-varying operating conditions. The developed approach of the PI controller adaptation is presented and discussed. The feasibility of the control system for tracking a desired biomass growth time profile is demonstrated with numerical simulations and fed-batch culture E.coli control experiments in a laboratory-scale bioreactor. The procedure was cross-validated with the open-loop digital twin SGR estimator, as well as with the adaptive control of the SGR, by tracking a desired setpoint time profile. The digital twin behavior statistically showed less of a bias when compared to SGR estimator performance. However, the adaptation—when using first principles—was outperformed 30 times by the model predictive controller in a robustness check scenario