Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae

Metabolic heat based specific growth rate estimators: Does the choice of estimation model influence the state of bioprocesses?

Accurate and reliable estimation of specific growth rate (μ) in real-time is pivotal for reliable process monitoring of a bioprocess and subsequent implementation of advanced control strategies. Gibbs free energy dissipation is imminent for any biological system, and the metabolic heat flow measurements (calorimetry) formed the basis for estimating μ. However, the rationale behind selecting a suitable μ estimator model based on calorimetric perspective remains unexplored. The present investigation addresses the notion behind the selection of an appropriate estimator for μ and the assessment of the estimator models was illustrated using different types of energy metabolism, namely, high exothermic and low exothermic processes. The results indicated that the μ values from the instantaneous heat flow estimator significantly deviated (10-fold higher) from the offline values for highly exothermic process. Notably, the cumulative heat-based estimator accurately estimated μ values on both types of energy metabolism with performance metrics <0.005 h-1.

Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Real-time metabolic heat-based specific growth rate soft sensor for monitoring and control of high molecular weight hyaluronic acid production by Streptococcus zooepidemicus

This present investigation addressing the metabolic bottleneck in synthesis of high MW HA by Streptococcus zooepidemicus and illustrates the application of calorimetric fed-batch control of µ at a narrower range. Feedforward (FF) and feedback (FB) control was devised to improve the molecular weight (MW) of HA production by S. zooepidemicus. Metabolic heat measurements (Fermentation calorimetry) were modeled to decipher real-time specific growth rate, [Formula: see text] was looped into the PID circuit, envisaged to control [Formula: see text] to their desired setpoint values 0.05 [Formula: see text], 0.1 [Formula: see text], and 0.15 [Formula: see text] respectively. Similarly, a predetermined exponential feed rate irrespective of real-time µ was carried out in FF strategy. The developed FB strategy established a robust control capable of maintaining the specific growth rate (µ) close to the [Formula: see text] value with a minimal tracking error. Exponential feed rate carried out with a lowest [Formula: see text] of 0.05 [Formula: see text] showed an improved MW of HA to 2.98 MDa and 2.94 MDa for the FF and FB-based control strategies respectively. An optimal HA titer of 4.73 g/L was achieved in FF control strategy at [Formula: see text]. Superior control of µ at low [Formula: see text] value was observed to influence HA polymerization positively by yielding an improved MW and desired polydispersity index (PDI) of HA. PID control offers advantage over conventional fed-batch method to synthesize HA at an improved MW. Calorimetric signal-based µ control by PID negates adverse effects due to the secretion of other end products albeit maintaining regular metabolic activities. KEY POINTS: First report to compare HA productivities by feedforward and feedback control strategy. Inherent merits of regulating µ at narrower range were entailed. Relationship between operating µ and HA molecular weight was discussed.

Novel Strategy for the Calorimetry-Based Control of Fed-Batch Cultivations of Saccharomyces cerevisiae

Typical controllers for fed-batch cultivations are based on the estimation and control of the specific growth rate in real time. Biocalorimetry allows one to measure a heat signal proportional to the substrate consumed by cells. The derivative of this heat signal is usually used to evaluate the specific growth rate, introducing noise to the resulting estimate. To avoid this, this study investigated a novel controller based directly on the heat signal. Time trajectories of the heat signal setpoint were modelled for different specific growth rates, and the controller was set to follow this dynamic setpoint. The developed controller successfully followed the setpoint during aerobic cultivations of Saccharomyces cerevisiae, preventing the Crabtree effect by maintaining low glucose concentrations. With this new method, fed-batch cultivations of S. cerevisiae could be reliably controlled at specific growth rates between 0.075 h−1 and 0.20 h−1, with average root mean square errors of 15 ± 3%.

Engineered Polyploid Yeast Strains Enable Efficient Xylose Utilization and Ethanol Production in Corn Hydrolysates

The reported haploid Saccharomyces cerevisiae strain F106 can utilize xylose for ethanol production. After a series of XR and/or XDH mutations were introduced into F106, the XR-K270R mutant was found to outperform others. The corresponding haploid, diploid, and triploid strains were then constructed and their fermentation performance was compared. Strains F106-KR and the diploid produced an ethanol yield of 0.45 and 0.48 g/g total sugars, respectively, in simulated corn hydrolysates within 36 h. Using non-detoxicated corncob hydrolysate as the substrate, the ethanol yield with the triploid was approximately sevenfold than that of the diploid at 40°C. After a comprehensive evaluation of growth on corn stover hydrolysates pretreated with diluted acid or alkali and different substrate concentrations, ethanol yields of the triploid strain were consistently higher than those of the diploid using acid-pretreatment. These results demonstrate that the yeast chromosomal copy number is positively correlated with increased ethanol production under our experimental conditions.

Serial propagation in water-in-oil emulsions selects for Saccharomyces cerevisiae strains with a reduced cell size or an increased biomass yield on glucose

In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.

Modeling and Exploiting Microbial Temperature Response

Temperature is an important parameter in bioprocesses, influencing the structure and functionality of almost every biomolecule, as well as affecting metabolic reaction rates. In industrial biotechnology, the temperature is usually tightly controlled at an optimum value. Smart variation of the temperature to optimize the performance of a bioprocess brings about multiple complex and interconnected metabolic changes and is so far only rarely applied. Mathematical descriptions and models facilitate a reduction in complexity, as well as an understanding, of these interconnections. Starting in the 19th century with the “primal” temperature model of Svante Arrhenius, a variety of models have evolved over time to describe growth and enzymatic reaction rates as functions of temperature. Data-driven empirical approaches, as well as complex mechanistic models based on thermodynamic knowledge of biomolecular behavior at different temperatures, have been developed. Even though underlying biological mechanisms and mathematical models have been well-described, temperature as a control variable is only scarcely applied in bioprocess engineering, and as a conclusion, an exploitation strategy merging both in context has not yet been established. In this review, the most important models for physiological, biochemical, and physical properties governed by temperature are presented and discussed, along with application perspectives. As such, this review provides a toolset for future exploitation perspectives of temperature in bioprocess engineering.

Preventing Overflow Metabolism in Crabtree-Positive Microorganisms through On-Line Monitoring and Control of Fed-Batch Fermentations

At specific growth rates above a particular critical value, Crabtree-positive microorganisms exceed their respiratory capacity and enter diauxic growth metabolism. Excess substrate is converted reductively to an overflow metabolite, resulting in decreased biomass yield and productivity. To prevent this scenario, the cells can be cultivated in a fed-batch mode at a growth rate maintained below the critical value, µcrit. This approach entails two major challenges: accurately estimating the current specific growth rate and controlling it successfully over the course of the fermentation. In this work, the specific growth rate of S. cerevisiae and E. coli was estimated from enhanced on-line biomass concentration measurements obtained with dielectric spectroscopy and turbidity. A feedforward-feedback control scheme was implemented to maintain the specific growth rate at a setpoint below µcrit, while on-line FTIR measurements provided the early detection of the overflow metabolites. The proposed approach is in line with the principles of Bioprocess Analytical Technology (BioPAT), and provides a means to increase the productivity of Crabtree-positive microorganisms.

Application of heat compensation calorimetry to an E. coli fed-batch process

The application of biocalorimetry to fermentation processes offers advantageous insights, while being less complex compared to other, sophisticated PAT solutions. Although the general concept is established, calorimetric methods vary in detail. In this work, a special approach, called heat compensation calorimetry, was applied to an E. coli fed-batch process. Much work has been done for batch processes, proving the validity and accuracy of this calorimetric mode. However, the adaption of this strategy to fed-batch processes has some implications. In the first section of this work, batch fermentations were performed, comparing heat capacity calorimetry to the compensation mode. Both processes showed very good agreement by means of growth behavior. The heat related differences, e.g. temperature profiles, were obvious. In addition, the impact of the chosen mode on the calculation of in-process heat transfer coefficients was shown. Finally, a fed-batch fermentation was performed. The compensation mode was kept sufficiently, up to the point where the metabolic heat production accelerated strongly. Controller tuning was a neuralgic point, which would have needed further optimization under these conditions. Nevertheless, in the present work it was possible to realize a working compensation process while demonstrating critical aspects that must be considered when establishing such approach.

Modeling of exo-inulinase biosynthesis by Kluyveromyces marxianus in fed-batch mode: correlating production kinetics and metabolic heat fluxes

A metabolic heat-based model was used for estimating the growth of Kluyveromyces marxianus, and the modified Luedeking-Piret kinetic model was used for describing the inulinase production kinetics. For the first time, a relationship was developed to relate inulinase production kinetics directly to metabolic heat generated, which corroborated well with the experimental data (with R ² values of above 0.9). It also demonstrated the predominantly growth-associated nature of the inulinase production with Luedeking-Piret parameters α and β, having values of 0.75 and 0.033, respectively, in the exponential feeding experiment. MATLAB was used for simulating the inulinase production kinetics which demonstrated the model's utility in performing real-time prediction of inulinase concentration with metabolic heat data as input. To validate the model predictions, a biocalorimetric (Bio RC1e) experiment for inulinase production by K. marxianus was performed. The inulinase concentration (IU/mL) values acquired from the model in were validated with the experimental values and the metabolic heat data. This modeling approach enabled the optimization, monitoring, and control of inulinase production process using the real-time biocalorimetric (Bio RC1e) data. Gas chromatography and mass spectrometry analysis were carried out to study the overflow metabolism taking place in K. marxianus inulinase production.

Calorespirometric feeding control enhances bioproduction from toxic feedstocks-Demonstration for biopolymer production out of methanol

The sustainable production of fuels and industrial bulk chemicals by microorganisms in biotechnological processes is promising but still facing various challenges. In particular, toxic substrates require an efficient process control strategy. Methanol, as an example, has the potential to become a major future feedstock due to its availability from fossil and renewable resources. However, besides being toxic, methanol is highly volatile. To optimize its dosage during microbial cultivations, an innovative, predictive process control strategy based on calorespirometry, i.e., simultaneous measurements of heat and CO2 emission rates, was developed. This rarely used technique allows an online-estimation of growth parameters such as the specific growth rate and substrate consumption rate as well as a detection of shifts in microbial metabolism thus enabling an adapted feeding for different phases of growth. The calorespirometric control strategy is demonstrated exemplarily for growth of the methylotrophic bacterium Methylobacterium extorquens on methanol and compared to alternative control strategies. Applying the new approach, the methanol concentration could be maintained far below a critical limit, while increased growth rates of M. extorquens and higher final contents of the biopolymer polyhydroxybutyrate were obtained. Biotechnol. Bioeng. 2016;113: 2113-2121. © 2016 Wiley Periodicals, Inc.

Maximizing biomass concentration in baker's yeast process by using a decoupled geometric controller for substrate and dissolved oxygen

Biomass production by baker's yeast in a fed-batch reactor depends on the metabolic regime determined by the concentration of glucose and dissolved oxygen in the reactor. Achieving high biomass concentration in turn is dependent on the dynamic interaction between the glucose and dissolved oxygen concentration. Taking this into account, we present in this paper the implementation of a decoupled input-output linearizing controller (DIOLC) for maximizing biomass in a fed-batch yeast process. The decoupling is based on the inversion of 2×2 input-output matrix resulting from global linearization. The DIOLC was implemented online using a platform created in LabVIEW employing a TCP/IP protocol via the reactor's built-in electronic system. An improvement in biomass yield by 23% was obtained compared to that using a PID controller. The results demonstrate superior capability of the DIOLC and that the cumulative effect of smoother control action contributes to biomass maximization.

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.