Bioprocess Control Research Articles

Elevated endoplasmic reticulum pH is associated with high growth and bisAb aggregation in CHO cells.

Chinese hamster ovary (CHO) bioprocesses, the dominant platform for therapeutic protein production, are increasingly used to produce complex multispecific proteins. Product quantity and quality are affected by intracellular conditions, but these are challenging to measure and often overlooked during process optimization studies. pH is known to impact quality attributes like protein aggregation across upstream and downstream processes, yet the effects of intracellular pH on cell culture performance are largely unknown. Recently, advances in protein biosensors have enabled investigations of intracellular environments with high spatiotemporal resolution. In this study, we integrated a fluorescent pH-sensitive biosensor into a bispecifc (bisAb)-producing cell line to investigate changes in endoplasmic reticulum pH (pHER). We then investigated how changes in lactate metabolism impacted pHER, cellular redox, and product quality in fed-batch and perfusion bioreactors. Our data show pHER rapidly increased during exponential growth to a maximum of pH 7.7, followed by a sharp drop in the stationary phase in all perfusion and fed-batch conditions. pHER decline in the stationary phase was driven by an apparent loss of cellular pH regulation that occurred despite differences in redox profiles. Finally, we found protein aggregate levels correlated most closely with pHER which provides new insights into product aggregate formation in CHO processes. An improved understanding of the intracellular changes impacting bioprocesses can ultimately help guide media optimizations, improve bioprocess control strategies, or provide new targets for cell engineering.

A review on development of enzymatic biosensors for industrial applications

Enzymatic biosensors are vital tools across various industries due to their high specificity, sensitivity, and rapid response times. They are extensively used in medical diagnostics for monitoring biomarkers like glucose and lactate, environmental monitoring to detect pollutants, food safety and quality assurance, bioprocess control in industrial fermentation, pharmaceutical drug development and quality control, soil health and agrochemical detection in agriculture, and for identifying biological warfare agents. However, several challenges limit their broader adoption and effectiveness in industrial settings. Key challenges include enzyme stability, as enzymes can denature under extreme environmental conditions, reducing sensor lifespan and performance. Variability in enzyme activity and immobilization techniques also leads to inconsistencies, affecting sensor reproducibility and reliability. Complex sample matrices, such as those in environmental and food samples, introduce interference that can affect sensor accuracy. Integrating biosensors into existing industrial processes is another significant challenge, requiring compatibility with automated systems and real-time data processing capabilities. Additionally, high production costs and scalability issues hinder widespread adoption. Regulatory and standardization barriers further complicate the development and deployment of enzymatic biosensors. Despite these limitations, advancements in materials science, nanotechnology, and bioengineering are paving the way for more robust and reliable biosensors. Innovations such as nanomaterial-based enzyme immobilization, synthetic biology for enzyme engineering, and advanced data analytics integration hold promise for overcoming current limitations. Collaborative efforts among researchers, industry stakeholders, and regulatory bodies are essential to accelerate the development and application of enzymatic biosensors in industrial settings. While enzymatic biosensors offer significant potential, addressing these challenges through ongoing research and technological advancements is crucial for their widespread implementation and optimization.

Model-Based Adaptive Control of Bioreactors—A Brief Review

This article summarizes the authors’ experiences in the development and application of the General Dynamical Model Approach related to adaptive linearizing control of biotechnological processes. Special attention has been given to some original, innovative solutions in model-based process control theory: new formalization of biotechnological process kinetics, derivation and tuning of the general software sensor of the full kinetics of biotechnological processes, and a general algorithm for fully adaptive linearizing control with software sensors. These theoretical solutions are the basis of three control strategies—fully adaptive control of the main substrate, partially adaptive control of intermediate metabolite, and recognition and stabilization of the desired physiological state based on the proposed theoretical solutions. Each strategy is illustrated in different case studies. The advantages and limitations of each of them are identified and discussed. The derived algorithms for monitoring and controlling the considered biotechnological processes are realized and included in a software platform named Interactive System for Education in Modelling and Control of Bioprocesses (InSEMCoBio). The InSEMCoBio modules and their main functions are discussed. The effectiveness of the proposed control strategies (achieving maximum productivity) has been proven through a series of simulation investigations of the considered case studies.

A MATLAB-based simulator for the study of process control of fed-batch yeast fermentations

This paper presents a MATLAB-based simulator for the study of process control of fed-batch yeast fermentations that meets the educational needs of undergraduate and graduate students. Against the background of challenges in interdisciplinary education, the paper examines the evolving environment of simulation tools in the field of bioprocesses. It emphasizes the need for interdisciplinarity to prepare students for the complexities of the modern biotechnology industry. Built with accessibility and flexibility in mind, the simulator offers a modular structure with a graphical user interface (GUI) for novice users and direct access to MATLAB functions for advanced users. This design choice ensures ease of use for students with different programming backgrounds and allows for adaptability to alternative software environments. The introductory sections provide a detailed overview of the simulator development, including the mathematical models that govern biomass growth kinetics, mass transfer, and process quality indicators. Selected process control strategies such as rule-based and feedforward approaches are incorporated in the simulator to enhance the learning experience by allowing students to experiment with different scenarios. The paper concludes by emphasizing the simulator's adaptability, modularity, and user-friendly interface as a valuable asset in educating students about the complexities of bioprocess control.

High-throughput G protein-coupled receptor-based autocrine screening for secondary metabolite production in yeast.

Biosensors are valuable tools in accelerating the test phase of the design-build-test-learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein-coupled receptor (GPCR)-based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR-based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water-in-oil-in-water double emulsion droplets, combined with analysis and sorting via a fluorescence-activated cell sorting machine. Employing tryptamine and serotonin as proof-of-concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin-producing strain over a nonproducing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems.

An event triggered control scheme for enhanced production of Escherichia coli and biomass concentration during fed-batch cultivation

Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7 %, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Controllable Fabrication of Polypyrrole Nanopillar/Hair Arrays by Oxygen Plasma Treatment and Their Applications as Antibacterial Flexible pH Sensors

AbstractFlexible pH sensors have attracted a great deal of attention in numerous applications, such as wearable healthcare devices, smart wound bandages, and other pH monitor sensors for chemical and bioprocess control. Herein, a facile approach to fabricate conducting polymer polypyrrole nanopillar arrays is proposed, and its potential as an advanced pH sensing interface is shown. Using oxygen plasma treatment as the etching tool, polypyrrole nanopillar/hair arrays can be prepared after etching 3D polypyrrole network structures. Conductive polypyrrole nanopillar arrays possessed unique advantages in sensing: their featured nanopillars has enhanced antibacterial properties; their structures showed excellent hydrophilicity and are favorable for interaction with analytes in small‐volume liquids. A flexible polypyrrole nanopillar array‐based potentiometric pH sensor integrated with a solid‐state reference electrode is further designed and fabricated. Superior performance with a near‐Nernstian response of 60.1±1.0 mV pH−1, excellent repeatability, high stability, and reproducibility is verified in the physiological pH range. The reliabilities of flexible and small‐volume sample testing, as well as the applicability for artificial sweat sample pH sensing, are examined. Provided with the advantages of antibacterial properties and hydrophilicity, the proposed flexible pH sensor is promising for trace analysis in biological, medical, and healthcare applications.

Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

Giving the cells what they need when they need it: Biosensor-based feeding control.

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

Predicting the performance of chain elongating microbiomes through flow cytometric fingerprinting.

As part of the circular bio-economy paradigm shift, waste management and valorisation practices have moved away from sanitation and towards the production of added-value compounds. Recently, the development of mixed culture bioprocess for the conversion of waste(water) to platform chemicals, such as medium chain carboxylic acids, has attracted significant interest. Often, the microbiology of these novel bioprocesses is less diverse and more prone to disturbances, which can lead to process failure. This issue can be tackled by implementing an advanced monitoring strategy based on the microbiology of the process. In this study, flow cytometry was used to monitor the microbiology of lactic acid chain elongation for the production of caproic acid, and assess its performance both qualitatively and quantitatively. Two continuous stirred tank reactors for chain elongation were monitored flow cytometrically for over 336 days. Through community typing, four specific community types could be identified and correlated to both a specific functionality and genotypic diversity. Additionally, the machine-learning algorithms trained in this study demonstrated the ability to predict production rates of, amongst others, caproic acid with high accuracy in the present (R² > 0.87) and intermediate accuracy in the near future (R² > 0.63). The identification of specific community types and the development of predictive algorithms form the basis of advanced bioprocess monitoring based on flow cytometry, and have the potential to improve bioprocess control and optimization, leading to better product quality and yields.

Bioprocess Modelling, Intensification, and Control


 
 
 Chemical bio-process intensification, driven by the need for sustainability and cost-effectiveness, has gained significant attention. This study explores dynamic intensification (DI), a strategy to enhance efficiency by modifying system dynamics, operation, and control. Two key approaches are investigated: intensification by design, involving deliberate changes to physical and chemical aspects, and intensification by control, optimizing process parameters and feedback loops.
 The research focuses on the Optimal Periodic Control (OPC) method, dating back to the 1960s but now revisited with modern technology. To investigate the feasibility and effectiveness of dynamic intensification, a comprehensive methodology encompassing modeling, optimization, linearization, dynamic intensification, and control was designed. This methodology ensures systematic and standardized testing of DI in various bioprocesses within the industry.
 Through rigorous case studies, such as Activated Sludge Model No. 1 (ASM1) and an antibiotic perfusion bioreactor, the study demonstrates the practical benefits of dynamic intensification, including enhanced process efficiency and control. Applying model predictive control (MPC) further improves process efficiency, and the intensified systems are thoroughly compared to their respective base cases.
 The insights gained from this research contribute to advancements in bioprocessing technologies across industries, paving the way for sustainable and efficient engineering practices. Engineers can leverage this knowledge and the developed methodology to optimize chemical bioprocesses through innovative intensification strategies, ultimately promoting more eco-friendly and economical bio-processes. The study's comprehensive methodology offers valuable insights and understanding for bioprocess engineers seeking to enhance system efficiency and contribute to a greener future.
 
 

Current challenges and recent advances on the path towards continuous biomanufacturing.

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Enhanced bioprocess control to advance the manufacture of mesenchymal stromal cell-derived extracellular vesicles in stirred-tank bioreactors.

Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) act as signaling mediators of cellular responses. However, despite representing a promising alternative to cell-based therapies, clinical translation of EVs is currently limited by their lack of scalability and standardized bioprocessing. Herein, we integrated scalable downstream processing protocols with standardized expansion of large numbers of viable cells in stirred-tank bioreactors to improve EV production. Higher EV yields were linked to EV isolation by tangential flow filtration followed by size exclusion chromatography, rendering 5 times higher number of EVs comparatively to density gradient ultracentrifugation protocols. Additionally, when compared to static culture, EV manufacture in bioreactors resulted in 2.2 higher yields. Highlighting the role of operating under optimal cell culture conditions to maximize the number of EVs secreted per cell, MSCs cultured at lower glucose concentration favored EV secretion. While offline measurements of metabolites concentration can be performed, in this work, Raman spectroscopy was also applied to continuously track glucose levels in stirred-tank bioreactors, contributing to streamline the selection of optimal EV collection timepoints. Importantly, MSC-derived EVs retained their quality attributes and were able to stimulate angiogenesis in vitro, therefore highlighting their promising therapeutic potential.

Methods and Analysis of Biological Contaminants in the Biomanufacturing Industry

The advent of bioprocessing has revolutionized the biomanufacturing industry, leading to the rise of biotherapeutics derived from biologic products such as chimeric antigen receptor (CAR) T-cells used for targeted cancer treatment and the Vero cell line for the production of viral vectors and vaccines. Despite these promising developments, most biologic products are characterized by fragile macromolecular structures that are heterogenous with a purity profile that varies with each batch making them susceptible to microorganism contamination. Regulatory oversight of biologic products is imperative to ensure adherence to good manufacturing practices and compliance with quality management systems. Current quality assurance protocols during production include monoclonality during cell line development, real-time monitoring of process parameters, flow cytometry for microbial monitoring, polymerase chain reaction, and immunoassay techniques to amplify DNA sequences related to bacterial or biological contaminants. FDA guidance recommends the implementation of process analytical technology within biomanufacturing production to measure critical quality parameters, which includes screening for potential biological contamination. Future advancements in bioprocess monitoring and control should capitalize on providing cheap, real-time, and sensitive detection. Biosensors, mass spectrometry, and polymerase chain reaction present robust, rapid, and real-time capabilities for multiplexed detection of contaminant analytes and have shown promise in meeting these needs. This review discusses the main biological contaminants of bioprocesses, European Union and FDA regulatory guidelines for monitoring and control within biologics production, existing methods and their limitations, and future advancements for biological contamination detection.

Linking morphological features to anammox communities in a partial nitritation and anammox (PN/A) biofilm reactor

Partial nitritation/anammox (PN/A) has been recognized as a cost-efficient process for wastewater nitrogen removal. The addition of carriers could help achieve biomass retention and enhance the treatment efficiency by forming the dense biofilm. However, accurately determining the abundance of anammox bacteria (AnAOB) to evaluate the biofilm development still remains challenging in practice without access to specialized facilities and experimental skills. In this study, we explored the feasibility of utilizing the morphological features of anammox biofilm as an indication of the biofilm development progression, and its correlation with microbial communities was also revealed. The time-series biofilms from an integrated fixed-film activated sludge (IFAS) system with stable PN/A performance were sampled representing the different biofilm development stages. The biofilm morphological features including color and texture were respectively quantified by red (R) coordinate and Local binary pattern (LBP) descriptor via image processing. Hierarchy clustering analysis proved that the extracted morphological descriptors could well distinguish the different stages (colonization, succession, and maturation) of biofilm development. The microbial community dynamics of time-series anammox biofilms were investigated using the amplicon sequence variant (ASV) analysis. Candidatus Brocadia, as the typical AnAOB, dominated in the whole communities of 16.3%–20.0%, moreover, the biofilm development was found to be driven by distinct Brocadia species. Linear regression evidenced that the Brocadia abundance could be directly correlated to the value of R and LBP, and the total variation of microbial communities could be significantly explained by the morphological features via redundancy analysis. This study demonstrates a new way to monitor the biofilm development by extracting the visible features of anammox aggregates, which can help facilitate the automated control of anammox-based bioprocess.

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.

Minos and Restless transposon insertion mutagenesis of psychrotrophic fungus for red pigment synthesis adaptive to normal temperature

The polar psychrotrophic fungus Geomyces sp. WNF-15A can produce high-quality natural red pigment for the potential use as edible pigment. However, it shows low-temperature-dependent synthesis of red pigment, which limits its large-scale industrial applications due to the difficult and high-cost bioprocess control. This study aims to develop transposon-mediated mutagenesis methods to generate mutants that are able to synthesize red pigment at normal temperature. Four transposable systems, including single and dual transposable systems, were established in this fungus based on the Minos from Drosophila hydei and the Restless from Tolypocladium inflatum. A total of 23 production-dominant mutants and 12 growth-dominant mutants were thus obtained by constructed transposable systems. At 14 °C and 20 °C, the MPS1 mutant strain achieved the highest level of red pigment (OD520 of 43.3 and 29.7, respectively), which was increased by 78.4% and 128.7% compared to the wild-type, respectively. Of note, 4 mutants (MPS1, MPS3, MPS4 and MPD1) successfully synthesized red pigment (OD520 of 5.0, 5.3, 4.7 and 4.9, respectively) at 25 °C, which broke the limit of the wild-type production under normal temperature. Generally, the dual transposable systems of Minos and Restless were more efficient than their single transposable systems for mutagenesis in this fungus. However, the positive mutation ratios were similar between the dual and single transposable systems for either Minos or Restless. This study provides alternative tools for genetic mutagenesis breeding of fungi from extreme environments.Graphical

Determination of recombinant thaumatin II secreted by Pichia pastoris using reversed-phase high-performance liquid chromatography

The manufacturing of recombinant proteins is increasing phenomenally, and yeasts serve as one of the most popular platforms for the large-scale production of these industrially relevant products. Protein analysis is a traditional step for the control and optimisation of such bioprocesses and often necessitates the combination of laborious sample treatments and expensive instruments. A hassle-free and cost-effective approach is always preferred for routine analyses and instigates a need for replacing these laborious tasks. In this study, a fast, simple, and reliable RP-HPLC method for the determination of the upcoming sweetener in the market, the sweet protein thaumatin was established. There is currently no reference method available for the quantification of this protein in fermentation samples. Due to an increasing industrial relevance foreseen for this sweetener, Pichia pastoris was used as a host for its bioproduction. The developed analytical method does not require a pre-treatment step of the biological sample and enables an easy quantification using a conventionally available HPLC system. The method facilitates the identification and quantification of recombinant thaumatin II and was validated according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines(ICH, 2021). The developed technique enables the monitoring of thaumatin levels in fermentations, thereby facilitating bioprocess control and optimisation.

Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.