Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

Abstract

Background Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. Results The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae , defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L −1 h −1 ), with a final ethanol concentration of 87 g L −1 and yield of 0.46 g ethanol g substrate −1 . The other fermentation strategies showed lower yields (close to 0.40 g ethanol g substrate −1 ) and ethanol productivity around 4.0 g L −1 h −1 . Conclusion The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

“…The system acquisition time was 0.2 s, with saving to hard disk every 10 s. For all the variables with online acquisition, an exponential filter associated with a median filter was added to smooth the readings and reduce noise from the sensors. Adapted from Mesquita et al (2019) [Color figure can be viewed at wileyonlinelibrary.com]…”

“…GSMs generate a complete profile of the cellular metabolic network at the steady‐state, from which the optimal environmental conditions are elucidated for both maximizing the synthesis of a product (Agren et al, 2013; Bideaux et al, 2016) and minimizing the production of unwanted metabolites (Bro et al, 2006; Gudmundsson & Nogales, 2015). This information can be efficiently transformed into appropriate process control strategies (Garcia‐Albornoz & Nielsen, 2013; Mesquita et al, 2019). However, the application of stoichiometric GSMs to transient processes must be performed with caution, implicitly assuming a pseudo‐steady state.…”

“…Another method consists of using surrogate or meta‐models, which are simpler but preserve the same basic responses of a full model (Papadopoulou et al, 2010). These models range from a simple mathematical correlation (Mesquita et al, 2019) to ones generated by more advanced deep learning techniques, such as artificial neural networks (ANNs) (Ibrahim et al, 2019; Mesquita et al, 2020).…”

Prospects for the therapeutic development of umbilical cord blood-derived mesenchymal stem cells

“…Recently developed manufacturing bioreactors, which are fully closed, controllable, and have scalable culture systems, have been used to monitor and control the metabolic state in real time including, dissolved oxygen, glucose, ammonia, pH, and lactate[ 143 - 145 ]. As the collection of large cell quantities takes a long time using manual methods, a reduction in collection times using automated equipment, increases the efficacy and consistency of the quality control.…”

Application of Acidophilic Microorganism in the Metal Enrichment of Electroplating Sludge Use the Membrane Bioreactor

“…Based on our previous research [18], under the optimized process conditions of 33.6 ℃ for the temperature, 3.1 m 3 /min for the aeration rate and 83 rpm for the stirring velocity, a high production efficiency of H2SO4 at pH 1.0 (≈850 L/h) was obtained [21]. The sulfuric acid produced by the membrane bioreactor was used for metal leaching experiments in electroplating sludge.…”