Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes

Adaptive laboratory evolution in a novel parallel shaken pH-auxostat

Adaptive laboratory evolution (ALE) is a widely used microbial strain development and optimization method. ALE experiments, to select for faster-growing strains, are commonly performed as serial batch cultivations in shake flasks, serum bottles, or microtiter plates or as continuous cultivations in bioreactors on a laboratory scale. To combine the advantages of higher throughput in parallel shaken cultures with continuous fermentations for conducting ALE experiments, a new Continuous parallel shaken pH-auxostat (CPA) was developed. The CPA consists of six autonomous parallel shaken cylindrical reactors, equipped with real-time pH control of the culture medium. The noninvasive pH measurement and control are realized by biocompatible pH sensor spots and a programmable pump module, to adjust the dilution rate of fresh medium for each reactor separately. Two different strains of the methylotrophic yeast Ogataea polymorpha were used as microbial model systems for parallel chemostat and pH-auxostat cultivations. During cultivation, the medium is acidified by the microbial activity of the yeast. For pH-auxostat cultivations, the growth-dependent acidification triggers the addition of fresh feed medium into the reactors, leading to a pH increase and thereby to the control of the pH to a predetermined set value. By controlling the pH to a predetermined set value, the dilution rate of the continuous cultivation is adjusted to values close to the washout point, in the range of the maximum specific growth rate of the yeast. The pH control was optimized by conducting a step-response experiment and obtaining tuned PI controller parameters by the Chien-Hrones-Reswick (CHR) PID tuning method. Two pH-auxostat cultivations were performed with two different O. polymorpha strains at high dilution rates for up to 18 days. As a result, up to 4.8-fold faster-growing strains were selected. The increased specific maximum growth rates of the selected strains were confirmed in subsequent batch cultivations.

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.

Model-Based Characterization of E. coli Strains with Impaired Glucose Uptake

The bacterium Escherichia coli is a widely used organism in biotechnology. For high space-time yields, glucose-limited fed-batch technology is the industry standard; this is because an overflow metabolism of acetate occurs at high glucose concentrations. As an interesting alternative, various strains with limited glucose uptake have been developed. However, these have not yet been characterized under process conditions. To demonstrate the efficiency of our previously developed high-throughput robotic platform, in the present work, we characterized three different exemplary E. coli knockout (KO) strains with limited glucose uptake capacities at three different scales (microtiter plates, 10 mL bioreactor system and 100 mL bioreactor system) under excess glucose conditions with different initial glucose concentrations. The extensive measurements of growth behavior, substrate consumption, respiration, and overflow metabolism were then used to determine the appropriate growth parameters using a mechanistic mathematical model, which allowed for a comprehensive comparative analysis of the strains. The analysis was performed coherently with these different reactor configurations and the results could be successfully transferred from one platform to another. Single and double KO mutants showed reduced specific rates for substrate uptake q_Smax and acetate production q_Apmax; meanwhile, higher glucose concentrations had adverse effects on the biomass yield coefficient Y_XSem. Additional parameters compared to previous studies for the oxygen uptake rate and carbon dioxide production rate indicated differences in the specific oxygen uptake rate q_Omax. This study is an example of how automated robotic equipment, together with mathematical model-based approaches, can be successfully used to characterize strains and obtain comprehensive information more quickly, with a trade-off between throughput and analytical capacity.

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as k_S , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate k_S . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of k_S values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity k_S of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A k_S of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial k_S estimation, sl-MSCC allows an affinity estimation by determining t_D at concentrations less or equal to the k_S value. Thus, sl-MSCC lays the foundation for strain-specific k_S estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

Biomass-specific rates as key performance indicators: A nitrogen balancing method for biofilm-based electrochemical conversion

Microbial electrochemical technologies (METs) employ microorganisms utilizing solid-state electrodes as either electron sink or electron source, such as in microbial electrosynthesis (MES). METs reaction rate is traditionally normalized to the electrode dimensions or to the electrolyte volume, but should also be normalized to biomass amount present in the system at any given time. In biofilm-based systems, a major challenge is to determine the biomass amount in a non-destructive manner, especially in systems operated in continuous mode and using 3D electrodes. We developed a simple method using a nitrogen balance and optical density to determine the amount of microorganisms in biofilm and in suspension at any given time. For four MES reactors converting CO₂ to carboxylates, >99% of the biomass was present as biofilm after 69 days of reactor operation. After a lag phase, the biomass-specific growth rate had increased to 0.12-0.16 days^-1. After 100 days of operation, growth became insignificant. Biomass-specific production rates of carboxylates varied between 0.08-0.37 mol_C mol_X ^-1d^-1. Using biomass-specific rates, one can more effectively assess the performance of MES, identify its limitations, and compare it to other fermentation technologies.

Inferring Microbial Biomass Yield and Cell Weight Using Probabilistic Macrochemical Modeling

Growth rates and biomass yields are key descriptors used in microbiology studies to understand how microbial species respond to changes in the environment. Of these, biomass yield estimates are typically obtained using cell counts and measurements of the feed substrate. These quantities are perturbed with measurement noise however. Perhaps most crucially, estimating biomass from cell counts, as needed to assess yields, relies on an assumed cell weight. Noise and discrepancies on these assumptions can lead to significant changes in conclusions regarding the microbes' response. This article proposes a methodology to address these challenges using probabilistic macrochemical models of microbial growth. It is shown that a model can be developed to fully use the experimental data, relax assumptions and greatly improve robustness to a priori estimates of the cell weight, and provides uncertainty estimates of key parameters. This methodology is demonstrated in the context of a specific case study and the estimation characteristics are validated in several scenarios using synthetically generated microbial growth data.

Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0

Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.

Recombinant Protein Production in E. coli Using the phoA Expression System

Auto-inducible promoter systems have been reported to increase soluble product formation in the periplasm of E. coli compared to inducer-dependent systems. In this study, we investigated the phosphate (PO4)-sensitive phoA expression system (pAT) for the production of a recombinant model antigen-binding fragment (Fab) in the periplasm of E. coli in detail. We explored the impact of non-limiting and limiting PO4 conditions on strain physiology as well as Fab productivity. We compared different methods for extracellular PO4 detection, identifying automated colorimetric measurement to be most suitable for at-line PO4 monitoring. We showed that PO4 limitation boosts phoA-based gene expression, however, the product was already formed at non-limiting PO4 conditions, indicating leaky expression. Furthermore, cultivation under PO4 limitation caused physiological changes ultimately resulting in a metabolic breakdown at PO4 starvation. Finally, we give recommendations for process optimization with the phoA expression system. In summary, our study provides very detailed information on the E. coli phoA expression system, thus extending the existing knowledge of this system, and underlines its high potential for the successful production of periplasmic products in E. coli.

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.

Cole-Cole modeling of real-time capacitance data for estimation of cell physiological properties in recombinant Escherichia coli cultivation

Real-time estimation of physiological properties of the cell during recombinant protein production would ensure enhanced process monitoring. In this study, we explored the application of dielectric spectroscopy to track the fed-batch phase of recombinant Escherichia coli cultivation for estimating the physiological properties, viz. cell diameter and viable cell concentration (VCC). The scanning capacitance data from the dielectric spectroscopy were pre-processed using moving average (MA). Later, it was modelled through a nonlinear theoretical Cole-Cole model and further solved using a global evolutionary genetic algorithm (GA). The parameters obtained from the GA were further applied for the estimation of the aforementioned physiological properties. The offline cell diameter and cell viability data were obtained from particle size analyzer and flow cytometry measurements to validate the Cole-Cole model. The offline VCC was calculated from the cell viability % from flow cytometry data and dry cell weight concentration (DCW). The Cole-Cole model predicted the cell diameter and VCC with an error of 1.03% and 7.72%, respectively. The proposed approach can enable the operator to take real-time process decisions in order to achieve desired productivity and product quality. This article is protected by copyright. All rights reserved.

Microbial technologies for biotherapeutics production: Key tools for advanced biopharmaceutical process development and control

Current trends in the biopharmaceutical market such as the diversification of therapies as well as the increasing time-to-market pressure will trigger the rethinking of bioprocess development and production approaches. Thereby, the importance of development time and manufacturing costs will increase, especially for microbial production. In the present review, we investigate three technological approaches which, to our opinion, will play a key role in the future of biopharmaceutical production. The first cornerstone of process development is the generation and effective utilization of platform knowledge. Building processes on well understood microbial and technological platforms allows to accelerate early-stage bioprocess development and to better condense this knowledge into multi-purpose technologies and applicable mathematical models. Second, the application of verified scale down systems and in silico models for process design and characterization will reduce the required number of large scale batches before dossier submission. Third, the broader availability of mathematical process models and the improvement of process analytical technologies will increase the applicability and acceptance of advanced control and process automation in the manufacturing scale. This will reduce process failure rates and subsequently cost of goods. Along these three aspects we give an overview of recently developed key tools and their potential integration into bioprocess development strategies.

Advances in monitoring and control of refolding kinetics combining PAT and modeling

Overexpression of recombinant proteins in Escherichia coli results in misfolded and non-active protein aggregates in the cytoplasm, so-called inclusion bodies (IB). In recent years, a change in the mindset regarding IBs could be observed: IBs are no longer considered an unwanted waste product, but a valid alternative to produce a product with high yield, purity, and stability in short process times. However, solubilization of IBs and subsequent refolding is necessary to obtain a correctly folded and active product. This protein refolding process is a crucial downstream unit operation-commonly done as a dilution in batch or fed-batch mode. Drawbacks of the state-of-the-art include the following: the large volume of buffers and capacities of refolding tanks, issues with uniform mixing, challenging analytics at low protein concentrations, reaction kinetics in non-usable aggregates, and generally low re-folding yields. There is no generic platform procedure available and a lack of robust control strategies. The introduction of Quality by Design (QbD) is the method-of-choice to provide a controlled and reproducible refolding environment. However, reliable online monitoring techniques to describe the refolding kinetics in real-time are scarce. In our view, only monitoring and control of re-folding kinetics can ensure a productive, scalable, and versatile platform technology for re-folding processes. For this review, we screened the current literature for a combination of online process analytical technology (PAT) and modeling techniques to ensure a controlled refolding process. Based on our research, we propose an integrated approach based on the idea that all aspects that cannot be monitored directly are estimated via digital twins and used in real-time for process control. KEY POINTS: • Monitoring and a thorough understanding of refolding kinetics are essential for model-based control of refolding processes. • The introduction of Quality by Design combining Process Analytical Technology and modeling ensures a robust platform for inclusion body refolding.

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.

Recombinant production of a hard-to-express membrane-bound cytochrome P450 in different yeasts-Comparison of physiology and productivity

Cytochrome P450s comprise one of the largest protein superfamilies. They occur in every kingdom of life and catalyse a variety of essential reactions. Their production is of utmost interest regarding biotransformation and structure‐function elucidation. However, they have proven hard to express due to their membrane anchor, their complex co‐factor requirements and their need for a redox‐partner. In our study, we investigated and compared different yeast strains for the production of the plant cytochrome P450 chalcone 3‐hydroxylase. To our knowledge, this is the first study evaluating different yeasts for the expression of this abundant and highly significant protein superfamily.

Saccharomyces cerevisiae and three different strains of Pichia pastoris expressing chalcone 3‐hydroxylase were cultivated in controlled bioreactor runs and evaluated regarding physiological parameters and expression levels of the cytochrome P450. Production differed significantly between the different strains and was found highest in the investigated P. pastoris MutS strain KM71H where 8 mg P450 per gram dry cell weight were detected. We believe that this host could be suitable for the expression of many eukaryotic, especially plant‐derived, cytochrome P450s as it combines high specific product yields together with straightforward cultivation techniques for achieving high biomass concentrations. Both factors greatly facilitate subsequent establishment of purification procedures for the cytochrome P450 and make the yeast strain an ideal platform for biotransformation as well.

Identification of Functional Bioprocess Model for Recombinant E. Coli Cultivation Process

The purpose of this study is to introduce an improved Luedeking–Piret model that represents a structurally simple biomass concentration approach. The developed routine provides acceptable accuracy when fitting experimental data that incorporate the target protein concentration of Escherichia coli culture BL21 (DE3) pET28a in fed-batch processes. This paper presents system identification, biomass, and product parameter fitting routines, starting from their roots of origin to the entropy-related development, characterized by robustness and simplicity. A single tuning coefficient allows for the selection of an optimization criterion that serves equally well for higher and lower biomass concentrations. The idea of the paper is to demonstrate that the use of fundamental knowledge can make the general model more common for technological use compared to a sophisticated artificial neural network. Experimental validation of the proposed model involved data analysis of six cultivation experiments compared to 19 experiments used for model fitting and parameter estimation.

Soft Sensor-Based Monitoring and Efficient Control Strategies of Biomass Concentration for Continuous Cultures of Haloferax mediterranei and Their Application to an Industrial Production Chain

Continuous bioprocessing using cell retention allows the achievement of high space-time yields for slow-growing organisms such as halophiles. However, the lack of efficient methods for monitoring and control limits the application of biotechnological processes in the industry. The aim of this study was to implement a control and online monitoring strategy for biomass in continuous cultures. For the first time, a feedforward cultivation strategy in a membrane-based cell retention system allowed to control the biomass concentration of the extreme halophilic Haloferax mediterranei at defined levels. Moreover, soft sensor-based biomass estimation allowed reliable monitoring of biomass online. Application of the combined monitoring and control strategy using industrial process water containing formate, phenol, aniline and 4,4′-methylenedianiline could for the first time demonstrate high throughput degradation in this extremophilic bioremediation process, obtaining degradation efficiencies of up to 100%. This process demonstrates the usefulness of continuous halophilic cultures in a circular economy application.

Generic estimator of biomass concentration for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures based on cumulative oxygen consumption rate

BACKGROUND

The focus of this study is online estimation of biomass concentration in fed-batch cultures. It describes a bioengineering software solution, which is explored for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures. The experimental investigation of both cultures presents experimental validation results since the start of the bioprocess, i.e. since the injection of inoculant solution into bioreactor. In total, four strains were analyzed, and 21 experiments were performed under varying bioprocess conditions, out of which 7 experiments were carried out with dosed substrate feeding. Development of the microorganisms' culture invariant generic estimator of biomass concentration was the main goal of this research.

RESULTS

The results show that stoichiometric parameters provide acceptable knowledge on the state of biomass concentrations during the whole cultivation process, including the exponential growth phase of both E. coli and S. cerevisiae cultures. The cell culture stoichiometric parameters are estimated by a procedure based on the Luedeking/Piret-model and maximization of entropy. The main input signal of the approach is cumulative oxygen uptake rate at fed-batch cultivation processes. The developed noninvasive biomass estimation procedure was intentionally made to not depend on the selection of corresponding bioprocess/bioreactor parameters.

CONCLUSIONS

The precision errors, since the bioprocess start, when inoculant was injected to a bioreactor, confirmed that the approach is relevant for online biomass state estimation. This included the lag and exponential growth phases for both E. coli and S. cerevisiae. The suggested estimation procedure is identical for both cultures. This approach improves the precision achieved by other authors without compromising the simplicity of the implementation. Moreover, the suggested approach is a candidate method to be the microorganisms' culture invariant approach. It does not depend on any numeric initial optimization conditions, it does not require any of bioreactor parameters. No numeric stability issues of convergence occurred during multiple performance tests. All this makes this approach a potential candidate for industrial tasks with adaptive feeding control or automatic inoculations when substrate feeding profile and bioreactor parameters are not provided.

Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors

Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA's process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model's predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. Graphical abstract.

The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling

The accurate estimation of cell growth or the substrate consumption rate is crucial for the understanding of the current state of a bioprocess. Rates unveil the actual cell status, making them valuable for quality-by-design concepts. However, in bioprocesses, the real rates are commonly not accessible due to analytical errors. We simulated Escherichia coli fed-batch fermentations, sampled at four different intervals and added five levels of noise to mimic analytical inaccuracy. We computed stepwise integral estimations with and without using moving average estimations, and smoothing spline interpolations to compare the accuracy and precision of each method to calculate the rates. We demonstrate that stepwise integration results in low accuracy and precision, especially at higher sampling frequencies. Contrary, a simple smoothing spline function displayed both the highest accuracy and precision regardless of the chosen sampling interval. Based on this, we tested three different options for substrate uptake rate estimations.

High level extracellular production of recombinant human interferon alpha 2b in glycoengineered Pichia pastoris: culture medium optimization, high cell density cultivation and biological characterization

AIMS

The present study was aimed at design of experiments (DoE)- and artificial intelligence-based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization.

METHODS AND RESULTS

The artificial neural network-genetic algorithm model exhibited improved huIFNα2b production and better predictability compared to response surface methodology. The optimized medium exhibited a fivefold increase in huIFNα2b titre compared to the complex medium. A maximum titre of huIFNα2b (436 mg l^-1 ) was achieved using the optimized medium in the bioreactor. Real-time capacitance data from dielectric spectroscopy were utilized to model the growth kinetics with unstructured models. Biological characterization by antiproliferative assay proved that the purified recombinant huIFNα2b was biologically active, exhibiting growth inhibition on breast cancer cell line.

CONCLUSIONS

Culture medium optimization resulted in enhanced production of huIFNα2b in glycoengineered P. pastoris at both shake flask and bioreactor level. The purified huIFNα2b was found to be N-glycosylated and biologically active.

SIGNIFICANCE AND IMPACT OF THE STUDY

DoE-based medium optimization strategy significantly improved huIFNα2b production. The antiproliferative activity of huIFNα2b substantiates its potential scope for application in cancer therapy.

A novel nicotinamide adenine dinucleotide control strategy for increasing the cell density of Haemophilus parasuis

Haemophilus parasuis is the causative agent of Glässer's disease and is a major source of economic losses in the swine industry each year. To enhance the production of an inactivated vaccine against H. parasuis, the availability of nicotinamide adenine dinucleotide (NAD) must be carefully controlled to ensure a sufficiently high cell density of H. parasuis. In the present study, the real-time viable cell density of H. parasuis was calculated based on the capacitance of the culture. By assessing the relationship between capacitance and viable cell density/NAD concentration, the NAD supply rate could be adjusted in real time to maintain the NAD concentration at a set value based on the linear relationship between capacitance and NAD consumption. The linear relationship between cell density and addition of NAD indicated that 7.138 × 10⁹ NAD molecules were required to satisfy per cell growth. Five types of NAD supply strategy were used to maintain different NAD concentration for H. parasuis cultivation, and the results revealed that the highest viable cell density (8.57, OD₆₀₀ ) and cell count (1.57 × 10¹⁰ CFU/mL) were obtained with strategy III (NAD concentration maintained at 30 mg/L), which were 1.46- and 1.45- times more, respectively, than cultures with using NAD supply strategy I (NAD concentration maintained at 10 mg/L). An extremely high cell density of H. parasuis was achieved using this NAD supply strategy, and the results demonstrated a convenient and reliable method for determining the real-time viable cell density relative to NAD concentration. Moreover, this method provides a theoretical foundation and an efficient approach for high cell density cultivation of other auxotroph bacteria.

Efficient Development of a Mixed Feed Process for Pichia pastoris

Pichia pastoris is one of the most important host organisms for the recombinant production of proteins in industrial biotechnology. A prominent promoter system for recombinant protein production in P. pastoris is the promoter of alcohol oxidase (P_AOX1) which is induced by methanol, but repressed by several other carbon sources, like glucose and glycerol. Thus, typical cultivation strategies for such P. pastoris strains describe two different phases: growth on a carbon source, like glycerol, to get a high biomass concentration, followed by the induction of recombinant protein production by methanol. However, cells barely grow on methanol resulting in only moderate productivity in such bioprocesses. To enhance productivity, it is common to employ mixed substrate feeding strategies. The knowledge of certain strain-specific parameters is required to be able to set up such mixed feed fed-batch cultivations to avoid methanol accumulation and guarantee highest productivity. Here, we present an efficient strategy comprising only one experiment to determine the settings of such a mixed feed system based on the physiology of the respective yeast strain.

E. coli HMS174(DE3) is a sustainable alternative to BL21(DE3)

Background

Escherichia coli is one of the most widely used hosts for recombinant protein production in academia and industry. Strain BL21(DE3) is frequently employed due to its advantageous feature of lacking proteases which avoids degradation of target protein. Usually it is used in combination with the T7-pET system where induction is performed by one point addition of IPTG. We recently published a few studies regarding lactose induction in BL21(DE3) strains. BL21(DE3) can only take up the glucose-part of the disaccharide when fed with lactose. However, initially additional glucose has to be supplied as otherwise the ATP-related lactose uptake barely happens. Yet, as lactose is an inexpensive compound compared to glucose and IPTG, a new induction strategy by a lactose-only feed during induction seems attractive. Thus, we investigated this idea in the galactose metabolizing strain HMS174(DE3).

Results

We show that strain HMS174(DE3) can be cultivated on lactose as sole carbon source during induction. We demonstrate that strain HMS174(DE3) exhibits higher product and biomass yields compared to BL21(DE3) when cultivated in a lactose fed-batch. More importantly, HMS174(DE3) cultivated on lactose even expresses more product than BL21(DE3) in a standard IPTG induced glucose fed-batch at the same growth rate. Finally, we demonstrate that productivity in HMS174(DE3) lactose-fed batch cultivations can easily be influenced by the specific lactose uptake rate (q_s,lac). This is shown for two model proteins, one expressed in soluble form and one as inclusion body.

Conclusions

As strain HMS174(DE3) expresses even slightly higher amounts of target protein in a lactose fed-batch than BL21(DE3) in a standard cultivation, it seems a striking alternative for recombinant protein production. Especially for large scale production of industrial enzymes cheap substrates are essential. Besides cost factors, the strategy allows straight forward adjustment of specific product titers by variation of the lactose feed rate.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-1016-6) contains supplementary material, which is available to authorized users.

Process performance and modelling of anaerobic digestion using source-sorted organic household waste

Three distinctive start-up strategies of biogas reactors fed with source-sorted organic fraction of municipal solid waste were investigated to reveal the most reliable procedure for rapid process stabilization. Moreover, the experimental results were compared with mathematical modeling outputs. The initial inoculations to start-up the reactors were 10, 50 and 100% of the final working volume. While a constant feeding rate of 7.8gVS/d was considered for the control reactor, the organic loading rate for fed-batch reactors with 10 and 50% inoculation was progressively increased during a period of 60 and 13days, respectively. The results clearly demonstrated that an exponentially feeding strategy, considering 50% inoculation relative to final volume, can significantly decrease the alternatively prolonged period to reach steady conditions, as observed by high biogas and methane production rates. The combination of both experimental and modelling/simulation succeeded in optimizing the start-up process for anaerobic digestion of biopulp under mesophilic conditions.

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Teaching an old pET new tricks: tuning of inclusion body formation and properties by a mixed feed system in E. coli

Against the outdated belief that inclusion bodies (IBs) in Escherichia coli are only inactive aggregates of misfolded protein, and thus should be avoided during recombinant protein production, numerous biopharmaceutically important proteins are currently produced as IBs. To obtain correctly folded, soluble product, IBs have to be processed, namely, harvested, solubilized, and refolded. Several years ago, it was discovered that, depending on cultivation conditions and protein properties, IBs contain partially correctly folded protein structures, which makes IB processing more efficient. Here, we present a method of tailored induction of recombinant protein production in E. coli by a mixed feed system using glucose and lactose and its impact on IB formation. Our method allows tuning of IB amount, IB size, size distribution, and purity, which does not only facilitate IB processing, but is also crucial for potential direct applications of IBs as nanomaterials and biomaterials in regenerative medicine.

Engineering strategies for enhanced production of protein and bio-products in Pichia pastoris: A review

Pichia pastoris has been recognized as one of the most industrially important hosts for heterologous protein production. Despite its high protein productivity, the optimization of P. pastoris cultivation is still imperative due to strain- and product-specific challenges such as promoter strength, methanol utilization type and oxygen demand. To address the issues, strategies involving genetic and process engineering have been employed. Optimization of codon usage and gene dosage, as well as engineering of promoters, protein secretion pathways and methanol metabolic pathways have proved beneficial to innate protein expression levels. Large-scale production of proteins via high cell density fermentation additionally relies on the optimization of process parameters including methanol feed rate, induction temperature and specific growth rate. Recent progress related to the enhanced production of proteins in P. pastoris via various genetic engineering and cultivation strategies are reviewed. Insight into the regulation of the P. pastoris alcohol oxidase 1 (AOX1) promoter and the development of methanol-free systems are highlighted. Novel cultivation strategies such as mixed substrate feeding are discussed. Recent advances regarding substrate and product monitoring techniques are also summarized. Application of P. pastoris to the production of biodiesel and other value-added products via metabolic engineering are also reviewed. P. pastoris is becoming an indispensable platform through the use of these combined engineering strategies.

Finite Time Estimation for Switched Nonlinear Systems: Application to Stirred Tank Bioreactor

This work presents the design of a class of finite time observer applied to a nonlinear switched system. The proposed observer is applied to a stirred batch anaerobic bioreactor, described by classic mass balances, where a sulfate-reducing process took place and kinetic regimen alteration is induced by a change in the carbon source. The proposed observer has a simple structure and under an adequate choosing of the observer´s gain the proposed methodology cancels the upper bounds of the system under the different kinetic regimens inducing the required finite time convergence. The performance of the proposed observer is showed via numerical simulations and for comparison purposes a sliding-mode observer is also implemented, both of them are conducted and experimentally corroborated. The convergence of the observer is done with a simple analysis of the estimation error dynamic equations for all the corresponding subspaces and is showed that the convergence of the estimator is reached under the required conditions.

Synthesis and analysis of separation networks for the recovery of intracellular chemicals generated from microbial-based conversions

BACKGROUND

Bioseparations can contribute to more than 70% in the total production cost of a bio-based chemical, and if the desired chemical is localized intracellularly, there can be additional challenges associated with its recovery. Based on the properties of the desired chemical and other components in the stream, there can be multiple feasible options for product recovery. These options are composed of several alternative technologies, performing similar tasks. The suitability of a technology for a particular chemical depends on (1) its performance parameters, such as separation efficiency; (2) cost or amount of added separating agent; (3) properties of the bioreactor effluent (e.g., biomass titer, product content); and (4) final product specifications. Our goal is to first synthesize alternative separation options and then analyze how technology selection affects the overall process economics. To achieve this, we propose an optimization-based framework that helps in identifying the critical technologies and parameters.

RESULTS

We study the separation networks for two representative classes of chemicals based on their properties. The separation network is divided into three stages: cell and product isolation (stage I), product concentration (II), and product purification and refining (III). Each stage exploits differences in specific product properties for achieving the desired product quality. The cost contribution analysis for the two cases (intracellular insoluble and intracellular soluble) reveals that stage I is the key cost contributor (>70% of the overall cost). Further analysis suggests that changes in input conditions and technology performance parameters lead to new designs primarily in stage I.

CONCLUSIONS

The proposed framework provides significant insights for technology selection and assists in making informed decisions regarding technologies that should be used in combination for a given set of stream/product properties and final output specifications. Additionally, the parametric sensitivity provides an opportunity to make crucial design and selection decisions in a comprehensive and rational manner. This will prove valuable in the selection of chemicals to be produced using bioconversions (bioproducts) as well as in creating better bioseparation flow sheets for detailed economic assessment and process implementation on the commercial scale.

Different cultivation methods to acclimatise ammonia-tolerant methanogenic consortia

Bioaugmentation with ammonia tolerant-methanogenic consortia was proposed as a solution to overcome ammonia inhibition during anaerobic digestion process recently. However, appropriate technology to generate ammonia tolerant methanogenic consortia is still lacking. In this study, three basic reactors (i.e. batch, fed-batch and continuous stirred-tank reactors (CSTR)) operated at mesophilic (37°C) and thermophilic (55°C) conditions were assessed, based on methane production efficiency, incubation time, TAN/FAN (total ammonium nitrogen/free ammonia nitrogen) levels and maximum methanogenic activity. Overall, fed-batch cultivation was clearly the most efficient method compared to batch and CSTR. Specifically, by saving incubation time up to 150%, fed-batch reactors were acclimatised to nearly 2-fold higher FAN levels with a 37%-153% methanogenic activity improvement, compared to batch method. Meanwhile, CSTR reactors were inhibited at lower ammonia levels. Finally, specific methanogenic activity test showed that hydrogenotrophic methanogens were more active than aceticlastic methanogens in all FAN levels above 540mgNH₃-NL^-1.

Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production

This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.

A novel method to recover inclusion body protein from recombinant E. coli fed-batch processes based on phage ΦX174-derived lysis protein E

Production of recombinant proteins as inclusion bodies is an important strategy in the production of technical enzymes and biopharmaceutical products. So far, protein from inclusion bodies has been recovered from the cell factory through mechanical or chemical disruption methods, requiring additional cost-intensive unit operations. We describe a novel method that is using a bacteriophage-derived lysis protein to directly recover inclusion body protein from Escherichia coli from high cell density fermentation process: The recombinant inclusion body product is expressed by using a mixed feed fed-batch process which allows expression tuning via adjusting the specific uptake rate of the inducing substrate. Then, bacteriophage ΦX174-derived lysis protein E is expressed to induce cell lysis. Inclusion bodies in empty cell envelopes are harvested via centrifugation of the fermentation broth. A subsequent solubilization step reveals the recombinant protein. The process was investigated by analyzing the impact of fermentation conditions on protein E-mediated cell lysis as well as cell lysis kinetics. Optimal cell lysis efficiencies of 99% were obtained with inclusion body titers of >2.0 g/l at specific growth rates higher 0.12 h^-1 and inducer uptake rates below 0.125 g/(g × h). Protein E-mediated cell disruption showed a first-order kinetics with a kinetic constant of -0.8 ± 0.3 h^-1. This alternative inclusion body protein isolation technique was compared to the one via high-pressure homogenization. SDS gel analysis showed 10% less protein impurities when cells had been disrupted via high-pressure homogenization, than when empty cell envelopes including inclusion bodies were investigated. Within this contribution, an innovative technology, tuning recombinant protein production and substituting cost-intensive mechanical cell disruption, is presented. We anticipate that the presented method will simplify and reduce the production costs of inclusion body processes to produce technical enzymes and biopharmaceutical products.

Mechanistic platform knowledge of concomitant sugar uptake in Escherichia coli BL21(DE3) strains

When producing recombinant proteins, the use of Escherichia coli strain BL21(DE3) in combination with the T7-based pET-expression system is often the method of choice. In a recent study we introduced a mechanistic model describing the correlation of the specific glucose uptake rate (q_s,glu) and the corresponding maximum specific lactose uptake rate (q_s,lac,max) for a pET-based E. coli BL21(DE3) strain producing a single chain variable fragment (scFv). We showed the effect of q_s,lac,max on productivity and product location underlining its importance for recombinant protein production. In the present study we investigated the mechanistic q_s,glu/q_s,lac,max correlation for four pET-based E. coli BL21(DE3) strains producing different recombinant products and thereby proved the mechanistic model to be platform knowledge for E. coli BL21(DE3). However, we found that the model parameters strongly depended on the recombinant product. Driven by this observation we tested different dynamic bioprocess strategies to allow a faster investigation of this mechanistic correlation. In fact, we succeeded and propose an experimental strategy comprising only one batch cultivation, one fed-batch cultivation as well as one dynamic experiment, to reliably determine the mechanistic model for q_s,glu/q_s,lac,max and get trustworthy model parameters for pET-based E. coli BL21(DE3) strains which are the basis for bioprocess development.

Enabler for process analytical technology implementation in Pichia pastoris fermentation: Fluorescence-based soft sensors for rapid quantitation of product titer

Rapid quantitation of product titer is a critical input for control of any bioprocess. This measurement, however, is marred by the myriad components that are present in the fermentation broth, often requiring extensive sample pretreatment before analysis. Spectroscopy techniques such as fluorescence spectroscopy are widely recognized as potential monitoring tools. Here, we investigate the possibility of using fluorescence of the culture supernatant as a potential at-line monitoring tool to measure the concentration of a recombinant therapeutic protein expressed in a Pichia pastoris fed-batch fermentation. We propose an integrated method wherein both the target protein and total protein concentrations are predicted using intrinsic riboflavin fluorescence and extrinsic fluorescence, respectively. The root mean square error for estimating the concentrations of the target protein (using riboflavin fluorescence) and total protein (using extrinsic fluorescence) have been estimated to be <0.1 and <0.2, respectively. The proposed approach has been validated for two different biotherapeutic products, human serum albumin and granulocyte colony stimulating factor, that were expressed using Mut⁺ and Mut^s strains of P. pastoris, respectively. The proposed approach is rapid (1 min analysis time, 10 min total with at line sampling) and thus could be a significant enabler for process analytical technology implementation in Pichia fermentation.

Propagation of measurement accuracy to biomass soft-sensor estimation and control quality

In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.

The E. coli pET expression system revisited-mechanistic correlation between glucose and lactose uptake

Therapeutic monoclonal antibodies are mainly produced in mammalian cells to date. However, unglycosylated antibody fragments can also be produced in the bacterium Escherichia coli which brings several advantages, like growth on cheap media and high productivity. One of the most popular E. coli strains for recombinant protein production is E. coli BL21(DE3) which is usually used in combination with the pET expression system. However, it is well known that induction by isopropyl β-d-1-thiogalactopyranoside (IPTG) stresses the cells and can lead to the formation of insoluble inclusion bodies. In this study, we revisited the pET expression system for the production of a novel antibody single-chain variable fragment (scFv) with the goal of maximizing the amount of soluble product. Thus, we (1) investigated whether lactose favors the recombinant production of soluble scFv compared to IPTG, (2) investigated whether the formation of soluble product can be influenced by the specific glucose uptake rate (q_s,glu) during lactose induction, and (3) determined the mechanistic correlation between the specific lactose uptake rate (q_s,lac) and q_s,glu. We found that lactose induction gave a much greater amount of soluble scFv compared to IPTG, even when the growth rate was increased. Furthermore, we showed that the production of soluble protein could be tuned by varying q_s,glu during lactose induction. Finally, we established a simple model describing the mechanistic correlation between q_s,lac and q_s,glu allowing tailored feeding and prevention of sugar accumulation. We believe that this mechanistic model might serve as platform knowledge for E. coli.