A hierarchical state estimation and control framework for monitoring and dissolved oxygen regulation in bioprocesses

ClearColi as a platform for untagged pneumococcal surface protein A production: cultivation strategy, bioreactor culture, and purification

Abstract

Several studies have searched for new antigens to produce pneumococcal vaccines that are more effective and could provide broader coverage, given the great number of serotypes causing pneumococcal diseases. One of the promising subunit vaccine candidates is untagged recombinant pneumococcal surface protein A (PspA4Pro), obtainable in high quantities using recombinant Escherichia coli as a microbial factory. However, lipopolysaccharides (LPS) present in E. coli cell extracts must be removed, in order to obtain the target protein at the required purity, which makes the downstream process more complex and expensive. Endotoxin-free E. coli strains, which synthesize a nontoxic mutant LPS, may offer a cost-effective alternative way to produce recombinant proteins for application as therapeutics. This paper presents an investigation of PspA4Pro production employing the endotoxin-free recombinant strain ClearColi® BL21(DE3) with different media (defined, auto-induction, and other complex media), temperatures (27, 32, and 37 °C), and inducers. In comparison to conventional E. coli cells in a defined medium, ClearColi presented similar PspA4Pro yields, with lower productivities. Complex medium formulations supplemented with salts favored PspA4Pro yields, titers, and ClearColi growth rates. Induction with isopropyl-β-d-thiogalactopyranoside (0.5 mM) and lactose (2.5 g/L) together in a defined medium at 32 °C, which appeared to be a promising cultivation strategy, was reproduced in 5 L bioreactor culture, leading to a yield of 146.0 mg PspA4Pro/g dry cell weight. After purification, the cell extract generated from ClearColi led to 98% purity PspA4Pro, which maintained secondary structure and biological function. ClearColi is a potential host for industrial recombinant protein production.

Key points

• ClearColi can produce as much PspA4Pro as conventional E. coli BL21(DE3) cells.

• 10.5 g PspA4Pro produced in ClearColi bioreactor culture using a defined medium.

• Functional PspA4Pro (98% of purity) was obtained in ClearColi bioreactor culture.

Model-Based Monitoring of Biotechnological Processes—A Review

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

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

Various bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L^-1 ·h^-1 and a yield of 0.48 g_ethanol .g_substrate ^-1 , which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L^-1 ·h^-1 and 0.35-0.40 g_ethanol ·g_substrate ^-1 , respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.

Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes

This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.

Giordano R, Sargo CR, Horta AC, Zangirolami TC. Cost analysis based on bioreactor cultivation conditions: Production of a soluble recombinant protein using Escherichia coli BL21(DE3)

The impact of cultivation strategy on the cost of recombinant protein production is crucial for defining cost-effective bioreactor operation conditions. This paper presents a methodology to estimate and compare cost impacts related to utilities as well as medium composition, using simple design equations and accessible data. Data from batch bioreactor cultures were used as case study involving the production of pneumococcal surface protein A, a soluble recombinant protein, employing E. coli BL21(DE3). Cultivation strategies and corresponding process costs covered a wide range of operational conditions, including different media, inducers, and temperatures. The core expenses were related to the medium and cooling. When the price of peptone was above the threshold value of US$ 30/kg, defined medium became the best choice. IPTG and temperatures around 32 °C led to shorter cultures and lower PspA4Pro production costs. The procedure offers a simple, accessible theoretical tool to identify cost-effective production strategies using bioreactors.

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.