Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty

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.

Enhancing and monitoring spore production in Clostridium butyricum using pH-based regulation strategy and a robust soft sensor based on back-propagation neural networks

Clostridium butyricum is a probiotic that forms anaerobic spores and plays a crucial role in regulating gut microbiota. However, the total viable cell count and spore yield of C. butyricum in industrial production are comparatively low. To this end, we investigated the metabolic characteristics of the strain and proposed three distinct pH regulation strategies for enhancing spore production. In addition, precise measurement of fermentation parameters such as substrate concentration, total viable cell count, and spore concentration is crucial for successful industrial probiotics production. Nevertheless, online measurement of these intricate parameters in the fermentation of C. butyricum poses a considerable challenge owing to the complex, nonlinear, multivariate, and strongly coupled characteristics of the production process. Therefore, we analyzed the capacitance and conductivity acquired from a viable cell sensor as the core parameters for the fermentation process. Subsequently, a robust soft sensor was developed using a seven-input back-propagation neural network model with input variables of fermentation time, capacitance, conductivity, pH, initial total sugar concentration, ammonium ion concentration, and calcium ion concentration. The model enables the online monitoring of total viable biomass count, substrate concentrations, and spore yield, and can be extended to similar fermentation processes with pH changes as a characteristic feature.

Intelligent approaches for sustainable management and valorisation of food waste

Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Metabolic Engineering for Valorization of Agri- and Aqua-Culture Sidestreams for Production of Nitrogenous Compounds by Corynebacterium glutamicum

Corynebacterium glutamicum is used for the million-ton-scale production of amino acids. Valorization of sidestreams from agri- and aqua-culture has focused on the production of biofuels and carboxylic acids. Nitrogen present in various amounts in sidestreams may be valuable for the production of amines, amino acids and other nitrogenous compounds. Metabolic engineering of C. glutamicum for valorization of agri- and aqua-culture sidestreams addresses to bridge this gap. The product portfolio accessible via C. glutamicum fermentation primarily features amino acids and diamines for large-volume markets in addition to various specialty amines. On the one hand, this review covers metabolic engineering of C. glutamicum to efficiently utilize components of various sidestreams. On the other hand, examples of the design and implementation of synthetic pathways not present in native metabolism to produce sought after nitrogenous compounds will be provided. Perspectives and challenges of this concept will be discussed.

Adaptation of Saccharomyces cerevisiae in a concentrated spent sulphite liquor waste stream for increased inhibitor resistance

The fermentation of spent sulphite liquor (SSL) from the pulping of hardwoods is limited by the combination of xylose, the primary fermentable sugar and high concentrations of microbial inhibitors that decrease the yeast fermentation ability. The inhibitor resistance phenotypes of xylose-capable Saccharomyces cerevisiae strains were therefore enhanced by combining rational engineering for multi-inhibitor tolerance, with adaptation in concentrated hardwood SSL as selective pressure. The adapted strains were assessed in fermentations with 60-80% v/v concentrated SSL under industrially relevant fermentation conditions. During adaptation, strains produced ethanol concentrations between 11.0 and 15.4 g/L in the range of that reported in literature. The adapted TFA40 and TP50 strains displayed enhanced inhibitor resistance phenotypes and were able to ferment xylose-rich SSL at pH below 5, exhibiting improved ethanol yields relative to the reference strain. Using yeast extract and peptone as nitrogen source in concentrated SSL fermentations further improved ethanol yields. However, strains exhibited a trade-off between resistance and ethanol productivity, indicating a carbon/energy cost for the expression of this inhibitor tolerance phenotype. KEY POINTS : • Achieved fermentation of xylose-rich hardwood spent sulphite liquor at pH below 5.0 • Adaptation of xylose-capable S. cerevisiae in concentrated spent sulphite liquor • Adapted strains exhibited enhanced inhibitor resistance phenotypes.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

Exploring the Potential of Corynebacterium glutamicum to Produce the Compatible Solute Mannosylglycerate

The compatible solute mannosylglycerate (MG) has exceptional properties in terms of protein stabilization and protection under salt, heat, and freeze-drying stresses as well as against protein aggregation. Due to these characteristics, MG possesses large potential for clinical and biotechnological applications. To achieve efficient MG production, Corynebacterium glutamicum was equipped with a bifunctional MG synthase (encoded by mgsD and catalyzing the condensation of 3-phosphoglycerate and GDP-mannose to MG) from Dehalococcoides mccartyi. The resulting strain C. glutamicum (pEKEx3 mgsD) intracellularly accumulated about 111 mM MG (60 ± 9 mg gCDW -1) with 2% glucose as a carbon source. To enable efficient mannose metabolization, the native manA gene, encoding mannose 6-phosphate isomerase, was overexpressed. Combined overexpression of manA and mgsD from two plasmids in C. glutamicum resulted in intracellular MG accumulation of up to ca. 329 mM [corresponding to 177 mg g cell dry weight (CDW) -1] with glucose, 314 mM (168 mg gCDW -1) with glucose plus mannose, and 328 mM (176 mg gCDW -1) with mannose as carbon source(s), respectively. The product was successfully extracted from cells by using a cold water shock, resulting in up to 5.5 mM MG (1.48 g L-1) in supernatants. The two-plasmid system was improved by integrating the mgsD gene into the manA-bearing plasmid and the resulting strain showed comparable production but faster growth. Repeated cycles of growth/production and extraction of MG in a bacterial milking-like experiment showed that cells could be recycled, which led to a cumulative MG production of 19.9 mM (5.34 g L-1). The results show that the newly constructed C. glutamicum strain produces MG from glucose and mannose and that a cold water shock enables extraction of MG from the cytosol into the medium.