Search Results (251)

Model-based approaches provide increasingly advanced opportunities for optimizing and accelerating bioprocess development. However, to accurately capture the complexity of biotechnological processes, continuous refinement of suitable models remains essential. A crucial gap in this field has been the lack of suitable model for describing Escherichia coli growth in cultivation media containing yeast extract, while accounting for key bioprocess parameters such as biomass, substrate, acetate, and oxygen. To address this, a published mechanistic macro-kinetic model for E. coli was extended with a set of mathematical equations that describe key aspects of the uptake of yeast extract. The underlying macro-kinetic approach is based on the utilization of amino acids in E. coli, where growth is primarily influenced by two distinct classes of amino acids. Using fed-batch cultivation data from an E. coli K-12 strain supplemented with yeast extract, it was demonstrated that the proposed model extensions were essential for accurately representing the bioprocess. This approach was further validated through fitting the model on cultivation data from five different yeast extracts sourced from various manufacturers. Additionally, the model enabled reliable predictions of growth dynamics across a range of yeast extract concentrations up to 20 g L⁻¹. Further differentiation of the data into batch and fed-batch revealed that for less complex datasets, such as those obtained from a batch phase, a simplified model can be sufficient. Due to its modular structure, the developed model provides the necessary flexibility to serve as a tool for the development, optimization, and control of E. coli cultivations with and without yeast extract. Full article

Lignocellulosic agricultural residues represent a rich source of potential feedstock for biorefinery applications, but their valorization remains challenging. The white-rot fungus Irpex lacteus J2 exhibited a promising degradation effect, but its molecular mechanisms of lignocellulose degradation remained largely uncharacterized. Here, we performed high-quality whole-genome sequencing and untargeted metabolomic profiling of I. lacteus J2 during the degradation of corn straw as the sole carbon source. The assembled I. lacteus J2 genome contained 14,647 protein-coding genes, revealing a rich genetic repertoire for biomass degradation and secondary metabolite synthesis. Comparative genomics showed high synteny (mean amino acid sequence identity 92.28%) with I. lacteus Irplac1. Untargeted metabolomic analysis unveiled a dynamic metabolic landscape during corn straw fermentation. Dominant metabolite classes included organic acids and derivatives (27.32%) and lipids and lipid-like molecules (25.40%), as well as heterocyclic compounds (20.41%). KEGG pathway-enrichment analysis highlighted significant activation of core metabolic pathways, with prominent enrichment in global metabolism (160 metabolites), amino acid metabolism (99 metabolites), carbohydrate metabolism (24 metabolites), and lipid metabolism (19 metabolites). Fermentation profiles at 3 and 15 days demonstrated substantial metabolic reprogramming, with up to 210 upregulated and 166 downregulated metabolites. Correlation analyses further revealed complex metabolic interdependencies and potential regulatory roles of key compounds. These integrated multi-omics insights significantly expand our understanding of the genetic basis and metabolic versatility, enabling I. lacteus J2 to efficiently utilize lignocellulose. Our findings position I. lacteus J2 as a robust model strain and provide a valuable foundation for developing advanced fungus-based strategies for sustainable bioprocessing and valorization of agricultural residues. Full article

This study focuses on determining the optimal fig and butyric acid concentrations for butanol production under thermophilic conditions. Waste fig is a potentially rich substrate in sugars, minerals, and vitamins, but it is insufficient for effective butanol formation when butyrate is not present in the media because butanol is produced by butyrate reduction. Therefore, butyric acid was supplemented gradually in certain concentrations to fig-containing fermentation broth. The best combination of butyric acid and fig was determined using the Box–Wilson statistical experiment design. Fig and butyric acid concentrations were set as independent variables, while butanol concentration was the objective function. When the concentrations of butyric acid and fig were near the middle of the ranges under inspection, more butanol was produced. Butanol production was the lowest as fig and butyric acid values got closer to the extremes, particularly at high concentrations. Maximum butanol of 0.32 g/L was obtained with 16 g fig/L and 1.6 g butyric acid/L. The quadratic model generated was found to be significant, and its reliability was tested with verification experiments with reproducible results. This study showed that butanol could be produced from butyrate-supplemented fig waste under thermophilic conditions with a consolidated bioprocessing approach. Full article

The efficient degradation of lignin from agricultural by-products is a critical step in the development of sustainable bioprocessing technologies for waste valorisation. Enzymatic degradation of kraft lignin performed with lignin peroxidase (LiP), manganese peroxidase (MnP), and laccase (Lac) was investigated. A response surface methodology (RSM) based on a Box–Behnken Design (BBD) was employed in order to optimise key process parameters including enzyme concentration, lignin concentration, pH, incubation temperature, and activator concentration. The surface plots were used to determine the best conditions for each enzyme in order to better degrade kraft lignin. Therefore, LiP needed a stronger acidic environment and moderate temperature, MnP needed an almost neutral pH and moderate temperature, and Lac needed a neutral pH and higher temperature. This work contributes to the development of smart agricultural waste management practices by combining enzymatic treatments with statistical modelling for process optimisation. This study provides a framework for lignin degradation that can be used as a starting point for diverse lignocellulosic by-product fragmentation, thus supporting a circular bioeconomy initiative in accordance with today’s trends. The optimised enzymatic parameters could help enhance efficiency, enable process standardisation across feedstocks, and support economically and environmentally sustainable industrial-scale lignin valorisation in integrated biorefineries. Full article

Traditional fermentation models often oversimplify kinetics by treating microbial populations as physiologically homogeneous. To address this, we introduce a novel framework that explicitly incorporates cellular fitness by distinguishing the metabolically active subpopulation (“productive cells”) responsible for biosynthesis. This approach integrates established growth models (First Order Plus Dead Time and Logistic) with a modified Luedeking–Piret model (MALP), which introduces a new differential equation to dynamically quantify productive cells. This modeling study relies exclusively on experimental data available in the literature; no new experimental work was conducted. Validated against four diverse fermentation systems from published datasets, the MALP model demonstrated superior predictive accuracy, achieving coefficients of determination (R² > 0.97) for metabolite kinetics. Sensitivity analysis identified time-delay and maintenance-associated parameters as dominant factors governing system behavior. The key contribution of this work is a mechanistic equation that universally captures the real-world dynamics of metabolite production, providing a more realistic and robust framework for modeling heterogeneous bioprocesses. Full article

Due to the regulated nature and purity standards of the bioprocess and biotechnology industries, the sector has seen comparatively less sustainable practices than other chemical industries have. The achievement of sustainability in microbial fermenter design requires that quantitative tools with links between process parameters and end-environmental outcomes are employed. This review begins with environmentally friendly metrics such as process mass intensity, water and energy intensity, and related indicators that act as a template for resource usage and waste generation assessment. The objective of this paper is to highlight the primary focus on computational fluid dynamics (CFD) applied to bioprocesses in aerated stirred bioreactors using Escherichia coli (E. coli). Second, the objective of this paper is to explore state-of-the-art CFD models and methods documented in the existing literature, providing a fundamental foundation for researchers to incorporate CFD modelling into biotechnological process development, while making these concepts accessible to non-specialists and addressing the research gap of linking CFD outputs with sustainability metrics and life cycle assessment techniques. Impeller rotational models such as sliding mesh are an accurate and commonly used method of modelling the rotation of stirring. Multiple different turbulence models are applied for the purpose of stirred bioreactors, with the family of k-ε models being the most used. Multiphase models such as Euler-Euler models in combination with population balance models and gas dispersion models to model bubble size distribution and bubble characteristics are typically used. Full article

Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability. Full article

This two-part study assesses the impact of biogas inhibition on large-scale waste-to-energy anaerobic digestion (WtE-AD) plants through techno-economic and life cycle assessment approaches. The first part addresses technical and economic aspects. An anaerobic co-digestion system using vegetable waste (FVW) and meat waste (MW) was operated at laboratory scale in a semi-continuous regime with daily feeding to establish a stable process and induce programmed failures causing methanogenic inhibition, achieved by removing MW from the reactor feed and drastically reducing the protein content. Experimental data, combined with bioprocess scale-up models and cost engineering methods, were then used to evaluate the effect of inhibition periods on the profitability of large-scale WtE-AD processes. In the experimental stage, the stable process achieved a yield of 521.5 ± 21 mL CH₄ g⁻¹ volatile solids (VS) and a biogas productivity of 0.965 ± 0.04 L L⁻¹ d⁻¹ (volume of biogas generated per reactor volume per day), with no failure risk detected, as indicated by the volatile fatty acids/total alkalinity ratio (VFA/TA, mg VFA L⁻¹/mg L⁻¹) and the VFA/productivity ratio (mg VFA L⁻¹/L L⁻¹ d⁻¹), both recognized as effective early warning indicators. However, during the inhibition period, productivity decreased by 64.26 ± 11.81% due to VFA accumulation and gradual TA loss. With the progressive reintroduction of the FVW:MW management and the addition of fresh inoculum to the reaction medium, productivity recovered to 96.7 ± 1.70% of its pre-inhibition level. In WtE-AD plants processing 60 t d⁻¹ of waste, inhibition events can reduce net present value (NPV) by up to 40.2% (from 0.98 M USD to 0.55 M USD) if occurring once per year. Increasing plant capacity (200 t d⁻¹), combined with higher revenues from waste management fees (99.5 USD t⁻¹) and favorable electricity markets allowing higher selling prices (up to 0.23 USD kWh⁻¹), can enhance resilience and offset inhibition impacts without significantly compromising profitability. These findings provide policymakers and industry stakeholders with key insights into the economic drivers influencing the competitiveness and sustainability of WtE-AD systems. Full article

Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability and transparency of fermentation monitoring. A Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM) was developed to predict Fermentation Percent (FP) and cumulative Fermentation Quantification (FQ) using multivariate time-series data obtained from modular sensor units (PBSU, GBSU, and FQSU). Fermentation conditions were systematically varied under controlled environments, and all data were securely transmitted to a Fermentation–Blockchain–Cloud System (FBCS) to ensure data integrity and traceability. The LSTM models trained on AAG1–3 datasets demonstrated high predictive accuracy, with coefficients of determination (R²) between 0.8547 and 0.9437, and the estimated FQ values showed strong concordance with actual measurements. These results underscore the feasibility of integrating AI-driven prediction models with decentralized data infrastructure for robust and scalable bioprocess control. Full article

The global increase in municipal and industrial wastewater generation has intensified the need for ecologically resilient and technologically advanced treatment systems. Although traditional biological treatment technologies are effective for organic load reduction, they often fail to remove recalcitrant xenobiotics such as pharmaceuticals, synthetic dyes, endocrine disruptors (EDCs), and microplastics (MPs). Engineered microbial consortia offer a promising and sustainable alternative owing to their metabolic flexibility, ecological resilience, and capacity for syntrophic degradation of complex pollutants. This review critically examines emerging strategies for enhancing microbial bioremediation in wastewater treatment systems (WWTS), focusing on co-digestion, biofilm engineering, targeted bioaugmentation, and incorporation of conductive materials to stimulate direct interspecies electron transfer (DIET). This review highlights how multi-omics platforms, including metagenomics, transcriptomics, and metabolomics, enable high-resolution community profiling and pathway reconstructions. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into bioprocess diagnostics facilitates real-time system optimization, predictive modeling of antibiotic resistance gene (ARG) dynamics, and intelligent bioreactor control. Persistent challenges, such as microbial instability, ARG dissemination, reactor fouling, and the absence of region-specific microbial reference databases, are critically analyzed. This review concludes with a translational pathway for the development of next-generation WWTS that integrate synthetic microbial consortia, AI-mediated biosensors, and modular bioreactors within the One Health and Circular Economy framework. Full article

This paper reviews the integration of artificial intelligence (AI) and machine learning in biorefineries and bioprocessing, with applications in biocatalysis, enzyme optimization, real-time monitoring, and quality assurance. AI contributes to predictive modeling and allows the precise forecasting of process outcomes, resource management, and energy utilization. AI models, including supervised, unsupervised, and reinforcement learning, support improvements in important bioprocess stages, such as fermentation, purification, and microbial biosynthesis. Digital twins and soft-sensing technologies enable real-time control and increase operational precision in complex bioprocess environments. Hybrid modeling integrates data-driven AI techniques with common scientific principles, improving scalability and adaptability under dynamic operational conditions. This review addresses challenges in AI implementation, such as data standardization, model transparency, and the need for interdisciplinary collaboration. The discussion concludes with future directions and sustainable AI strategies, highlighting the potential of AI to strengthen scalable, efficient, and environmentally sustainable biorefinery operations. These findings highlight how AI-driven methodologies improve operational efficiency, reduce resource waste, and facilitate sustainable innovation in bioprocesses, thereby strengthening sustainability within the bioeconomy. Full article

The biopharmaceutical industry is undergoing a fundamental transformation from traditional batch manufacturing to continuous manufacturing (CM) for recombinant drugs and biosimilars, driven by regulatory support through the International Council for Harmonization (ICH) Q13 guidance and compelling economic advantages. This comprehensive review examines the technical, economic, and regulatory aspects of implementing continuous manufacturing specifically for recombinant protein production and biosimilar development, synthesizing validated data from peer-reviewed research, regulatory sources, and global implementation case studies. The analysis demonstrates that continuous manufacturing offers substantial benefits, including a reduced equipment footprint of up to 70%, a 3- to 5-fold increase in volumetric productivity, enhanced product quality consistency, and facility cost reductions of 30–50% compared to traditional batch processes. Leading biomanufacturers across North America, Europe, and the Asia–Pacific region are successfully integrating perfusion upstream processes with connected downstream bioprocesses, enabling the fully end-to-end continuous manufacture of biopharmaceuticals with demonstrated commercial viability. The regulatory framework has been comprehensively established through ICH Q13 guidance and region-specific implementations across the FDA, EMA, PMDA, and emerging market authorities. This review provides a critical analysis of advanced technologies, including single-use perfusion bioreactors, continuous chromatography systems, real-time process analytical technology, and Industry 4.0 integration strategies. The economic modeling presents favorable return-on-investment profiles, accompanied by a detailed analysis of global market dynamics, regional implementation patterns, and supply chain integration opportunities. Full article

The sugarcane industry plays a crucial economic role worldwide, with sucrose and ethanol as its main products. However, its processing generates large volumes of by-products—such as bagasse, molasses, vinasse, and straw—that contain valuable components for biotechnological valorization. This review integrates approximately 100 original research articles published in JCR-indexed journals between 2015 and 2025, of which over 50% focus specifically on sugarcane-derived agroindustrial residues. The biotechnological approaches discussed include submerged fermentation, solid-state fermentation, enzymatic biocatalysis, and anaerobic digestion, highlighting their potential for the production of biofuels, enzymes, and high-value bioproducts. In addition to identifying current advances, this review addresses key technical challenges such as (i) the need for efficient pretreatment to release fermentable sugars from lignocellulosic biomass; (ii) the compositional variability of by-products like vinasse and molasses; (iii) the generation of metabolic inhibitors—such as furfural and hydroxymethylfurfural—during thermochemical processes; and (iv) the high costs related to inputs like hydrolytic enzymes. Special attention is given to detoxification strategies for inhibitory compounds and to the integration of multifunctional processes to improve overall system efficiency. The final section outlines emerging trends (2024–2025) such as the use of CRISPR-engineered microbial consortia, advanced pretreatments, and immobilization systems to enhance the productivity and sustainability of bioprocesses. In conclusion, the valorization of sugarcane by-products through biotechnology not only contributes to waste reduction but also supports circular economy principles and the development of sustainable production models. Full article

Bacterial nanocellulose (BNC) is a highly pure biopolymer with promising applications in the biomedical, food, and textile industries. However, the high production costs and low yields obtained in static conditions limit its scalability and industrial applications. This study addresses the sustainable production of BNC using a rotary disk bioreactor (RDB) and explores the use of grape pomace extract as an alternative carbon source for BNC production. Parameters such as the BNC production and biomass yield were evaluated using Komagataeibacter xylinus ATCC 53524 under different operational conditions (disk surface, rotation speed, and number of disks). The results showed that cellulose production increased using silicone-coated disks at 7–9 rpm (up to 2.72 g L⁻¹), while higher yields (5.23 g L⁻¹) were achieved when using grape pomace extract as the culture medium in comparison with conventional HS medium. FTIR and TGA characterizations confirmed that BNC obtained with grape pomace extract presents the same thermal and chemical characteristics than BNC produced with HS medium. This work provides insight into the feasibility of upscaling BNC production using a bioprocessing strategy, combining production in the RDB system and the use of an agro-industrial waste as a sustainable and cost-effective alternative. Full article

The environmental burden of conventional plastics has sparked interest in sustainable alternatives such as polyhydroxyalkanoates (PHAs). However, despite ample research in bioprocess development and the use of inexpensive waste streams, production costs remain a barrier to widespread commercialization. Complementary to this, genetic engineering offers another avenue for improved productivity. Cupriavidus necator stands out as a model host for PHA production due to its substrate flexibility, high intracellular polymer accumulation, and tractability to genetic modification. This review delves into metabolic engineering strategies that have been developed to enhance the production of poly(3-hydroxybutyrate) (PHB) and related copolymers in C. necator. Strategies include the optimization of central carbon flux, redox and cofactor balancing, adaptation to oxygen-limiting conditions, and fine-tuning of granule-associated protein expression and the regulatory network. This is followed by outlining engineered pathways improving the synthesis of PHB copolymers, PHBV, PHBHHx, and other emerging variants, emphasizing genetic modifications enabling biosynthesis based on unrelated single-carbon sources. Among these, enzyme engineering strategies and the establishment of novel artificial pathways are widely discussed. In particular, this review offers a comprehensive overview of promising engineering strategies, serving as a resource for future strain development and positioning C. necator as a valuable microbial chassis for biopolymer production at an industrial scale. Full article