Bioprocess Dynamics Research Articles

Enhanced low-cost lipopeptide biosurfactant production by Bacillus velezensis from residual glycerin.

Biosurfactants (BSFs) are molecules produced by microorganisms from various carbon sources, with applications in bioremediation and petroleum recovery. However, the production cost limits large-scale applications. This study optimized BSFs production by Bacillus velezensis (strain MO13) using residual glycerin as a substrate. The spherical quadratic central composite design (CCD) model was used to standardize carbon source concentration (30g/L), temperature (34°C), pH (7.2), stirring (239rpm), and aeration (0.775vvm) in a 5-L bioreactor. Maximum BSFs production reached 1527.6mg/L of surfactins and 176.88mg/L of iturins, a threefold increase through optimization. Microbial development, substrate consumption, concentration of BSFs, and surface tension were also evaluated on the bioprocess dynamics. Mass spectrometry Q-TOF-MS identified five surfactin and two iturin isoforms produced by B. velezensis MO13. This study demonstrates significant progress on BSF production using industrial waste as a microbial substrate, surpassing reported concentrations in the literature.

Modeling of bioprocess pre-stages for optimization of perfusion profiles and increased process understanding.

Improving bioprocess efficiency is important to reduce the current costs of biologics on the market, bring them faster to the market, and to improve the environmental footprint. The process intensification efforts were historically focused on the main stage, while intensification of pre-stages has started to gain attention only in the past decade. Performing bioprocess pre-stages in the perfusion mode is one of the most efficient options to achieve higher viable cell densities over traditional batch methods. While the perfusion-mode operation allows to reach higher viable cell densities, it also consumes large amount of medium, making it cost-intensive. The change of perfusion rate during a process (perfusion profile) determines how much medium is consumed, thereby running a process in optimal conditions is key to reduce medium consumption. However, the selection of the perfusion profile is often made empirically, without full understanding of bioprocess dynamics. This fact is hindering potential process improvements and means for cost reduction. In this study, we propose a process modeling approach to identify the optimal perfusion profile during bioprocess pre-stages. The developed process model was used internally during process development. We could reduce perfused medium volume by 25%-45% (project-dependent), while keeping the difference in the final cell within 5%-10% compared to the original settings. Additionally, the model helps to reduce the experimental workload by 30%-70% and to predict an optimal perfusion profile when process conditions need to be changed (e.g., higher seeding density, change of operating mode from batch to perfusion, etc.). This study demonstrates the potential of process modeling as a powerful tool for optimizing bioprocess pre-stages and thereby guiding process development, improving overall bioprocess efficiency, and reducing operational costs, while strongly reducing the need for wet-lab experiments.

Design of Observers with Sigmoid Function in Their Structure with Numerical Application to a Sulfate Reducing Bioreactor

Sulfate reducing bacteria (SRB) are currently important for the design of alternative technologies for bioremediation. The design of observers and/or controllers are necessary to estimate and/or control the dynamics of SRB bioprocesses. In this work, two adaptive observers with numerical application to the phenomenological model of sulfate removal by Desulfovibrio alaskesis 6SR are proposed to observe the biomass concentration with hydrogen sulfide measurement. The proposed observers contain in their structure an adaptivity term designed by the absolute error weighted by a sigmoid function. The proposed observers are compared using the ITE performance index. The numerical results indicate a better performance for the proposed estimators in contrast to the extended Luenberger observer.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification.

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.

Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes

Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicine. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many interdependent factors, as well as extremely limited data due to the high cost of experiments and the novelty of personalized bio-drugs. We develop a new model-based reinforcement learning framework that can achieve human-level control in low-data environments. A dynamic Bayesian network is used to capture causal interdependencies between factors and predict how the effects of different inputs propagate through the pathways of the bioprocess mechanisms. This model is interpretable and enables the design of process control policies that are robust against model risk. We present a computationally efficient, provably convergent stochastic gradient method for optimizing such policies. Validation is conducted on a realistic application with a multidimensional, continuous state variable. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was partially supported by National Institute of Standards and Technology [Grant 70NANB17H002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1232 .

Population balance modelling captures host cell protein dynamics in CHO cell cultures.

Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels.

Screening of CHO-K1 endogenous promoters for expressing recombinant proteins in mammalian cell cultures

For the production of recombinant protein therapeutics in mammalian cells, a high rate of gene expression is desired and hence strong viral-derived promoters are commonly used. However, they usually induce cellular stress and can be susceptible to epigenetic silencing. Endogenous promoters, which coordinates their activity with cellular and bioprocess dynamics while at the same time they maintain high expression levels, may help to avoid such drawbacks. In this work, new endogenous promoters were discovered based on high expression levels in RNA-seq data of CHO-K1 cells cultured in high density. The promoters of Actb, Ctsz, Hmox1, Hspa5, Vim and Rps18 genes were selected for generating new expression vectors for the production of recombinant proteins in mammalian cells. The in silico-derived promoter regions were experimentally verified and the majority showed transcriptional activity comparable or higher than CMV. Also, stable expression following a reduction of culture temperature was investigated. The characterized endogenous promoters (excluding Rps18) constitute a promising alternative to CMV promoter due to their high strength, long-term expression stability and integration into the regulatory network of the host cell. These promoters may also comprise an initial panel for designing cell engineering strategies and synthetic promoters, as well as for industrial cell line development.

Model uncertainty-based evaluation of process strategies during scale-up of biopharmaceutical processes

Reliable scale-up of biopharmaceutical production processes is key in Quality by Design. In this study, a model-based workflow is described to evaluate the bioprocess dynamics during process transfer and scale-up computationally. First, a mathematical model describes the bioprocess dynamics of different state variables (e.g., cell density, titer). Second, the model parameter probability distributions are determined at different scales due to measurement uncertainty. Third, the quantified parameter distributions are statistically compared to evaluate if the process dynamics have been changed. This workflow was tested for the scale-up of an antibody-producing CHO fed-batch process. Significant differences were identified between the process development (30 ml) and implementation (250 ml) scale, and the feeding strategy was validated using model-assisted Design of Experiments. Then, the validated process strategy was successfully scaled up to 2 l laboratory and 50 l pilot scale. In summary, the proposed workflow enables a knowledge-driven evaluation tool for bioprocess development.

Bioleaching of waste mobile phone printed circuit board in controlled redox potential compared to non-controlled redox potential

The present study was conducted to carry out bioleaching of waste mobile phone printed circuit board (WMPPCB) in a high redox potential-controlled fed-batch process compared to the low redox potential batch process. Concomitant additions of WMPPCB achieved high redox potential bioleaching of WMPPCB. The pH of the bioleaching slurry was maintained at 1.50 for both high and low redox potential experiment. High redox potential was controlled to allow more Fe-oxidizing activity compared to sulfur oxidation process. The primary element in the WMPPCB was Cu (26.3%) along with Ni, Zn, Al, etc., analyzed by XRF, while XRD revealed the presence of metal sulfide/oxides. Both experiments were carried out in a 2-L stirred tank reactor and regular measurement of pH, redox potential, planktonic viable cell count, Fe2+, Fe3+, Fe (total), SO42−, Cu2+ concentration. The change in pH and controlling with acid addition showed that fed-batch bioleaching consumes 1.35 times lower sulfuric acid per ton WMPPCB than the batch process. Both the batch and fed-batch bioleaching supported first-order reaction and also followed as similar type of intermediate leaching kinetics, which is neither chemically controlled nor diffusion-controlled type. The leaching yield of copper was found to be best (98–97%) in both leach residue and leach liquor analysis. The acid requirement/ton WMPPCB and the bio-residue weight loss state a better fed-batch process. The fed-batch bioleaching was comparatively better than batch process, when compared all bioprocess dynamics parameters and leaching yield of various metals.

Microbiology of municipal solid waste landfills: a review of microbial dynamics and ecological influences in waste bioprocessing.

Municipal solid waste landfills are widely used as a waste management tool and landfill microbiology is at the core of waste degradation in these ecosystems. This review investigates the microbiology of municipal solid waste landfills, focusing on the current state of knowledge pertaining to microbial diversity and functions facilitating in situ waste bioprocessing, as well as ecological factors influencing microbial dynamics in landfills. Bioprocessing of waste in municipal landfills emanates from substrate metabolism and co-metabolism by several syntrophic microorganisms, resulting in partial transformation of complex substrates into simpler polymeric compounds and complete mineralisation into inorganic salts, water and gases including the biofuel gas methane. The substrate decomposition is characterised by evolution and interactions of different bacterial, archaeal and fungal groups due to prevailing biotic and abiotic conditions in the landfills, allowing for hydrolytic, fermentative, acetogenic and methanogenic processes to occur. Application of metagenomics studies based on high throughput Next Generation Sequencing technique has advanced research on profiling of the microbial communities in municipal solid waste landfills. However, functional diversity and bioprocess dynamics, as well as key factors influencing the in situ bioprocesses involved in landfill waste degradation; the very elements that are key in determining the efficiency of municipal landfills as tools of waste management, remain ambiguous. Such gaps also hinder progressive understanding of fundamentals that underlie technology development based on waste biodegradation, and exploration of municipal waste as a bioresource.

Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization

The aim of this study was to estimate the kinetic parameters of alkaline protease production with consideration of different growth kinetic models in order to establish the most adequate one to describe the bioprocess dynamics in both batch and fed-batch modes. As a result, a particular method for parameter estimation is developed in this paper. In this method, a hybrid of two metaheuristic techniques, genetic algorithm (GA) and particle swarm optimization (PSO), which takes advantage of both techniques, is applied. In the suggested hybrid algorithm, GA provides the initial population for PSO and then PSO performs the improvement task. The method needs low-intensive computation and proved to be superior to the traditional methods. As a result of applying this method, it was found that the Contois model, in spite of its simplicity, provides a satisfactory agreement with the experimental data, whereas the adoption of more detailed models leads to negligible improvements of the fit. Finally, for comparison of performance of the hybrid algorithm, we used GA only and PSO only. It was shown that the proposed hybrid between meta-heuristics GA and PSO is more effective in terms of running time (two-fold faster) and solution quality, since it benefits from synergy. Also it was concluded that PSO performed slightly better than GA.

Dynamics of ethanol production from deproteinized whey by Kluyveromyces marxianus: An analysis about buffering capacity,thermal and nitrogen tolerance

The production of value-added products could be a valuable option for cheese wastewater management. However, this kind of study cannot just focus alone on getting the final product. This also necessitates studies on the dynamics of bioprocesses. With these as background, the present investigation aimed at evaluating the buffering capacity of deproteinized whey and effect of temperature and nitrogen source on ethanol yields from it. The batch fermentation conditions used to evaluate ethanol production were temperatures 30, 35, 40°C and pH 4.5, 5.0, 5.5, 6.0. To study the influence of nitrogen source on ethanol yield, a design matrix was applied using yeast extract and (NH4)2SO4.The final pH was analyzed to evaluate the buffering capacity. The results showed that the Kluyveromyces marxianus was thermotolerance to produce ethanol at 35 and 40°C, which was not observed at 30°C. Results also showed that the deproteinization procedure did not affect the buffering capacity of cheese whey. Finally, higher ethanol production was obtained using yeast extract (3% v/v). These results could be important for developing low-cost method for industrial production of ethanol from deproteinized whey.

An Observer-based Robust Control Strategy for Overflow Metabolism Cultures in Fed-Batch Bioreactors

An observer-based robust control strategy is proposed for controlling overflow metabolism cultures operated in fed-batch mode. In order to maximize the biomass productivity, the controller is designed to regulate the inhibitory by-product concentration at small levels keeping the substrate concentration close to its critical level. To this end, a reduced order nonlinear model of the bioprocess dynamics is obtained and a partial feedback linearizing strategy is applied. The resulting free linear dynamics is designed by means of a convex optimization problem aiming at mitigating the effects of non canceled nonlinearities and model uncertainties. An adaptive extended Luenberger observer is also designed for estimating the byproduct concentration from the measurements of biomass and substrate concentrations. Realistic numerical simulations demonstrate that the proposed observer based robust control strategy is able to maximize the biomass concentration despite large disturbances on the measurements (30 % for substrate and 15 % for biomass concentrations).

Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach

Microbial ecosystems are typified by diverse microbial interactions and competition. Consequently, the microbial networks and metabolic dynamics of bioprocesses catalyzed by these ecosystems are highly complex, and their visualization is regarded as essential to bioengineering technology and innovation. Here we describe a means of visualizing the variants in a microbial community and their metabolic profiles. The approach enables previously unidentified bacterial functions in the ecosystems to be elucidated. We investigated the anaerobic bioremediation of chlorinated ethene in a soil column experiment as a case study. Microbial community and dechlorination profiles in the ecosystem were evaluated by denaturing gradient gel electrophoresis (DGGE) fingerprinting and gas chromatography, respectively. Dechlorination profiles were obtained from changes in dechlorination by microbial community (evaluated by data mining methods). Individual microbes were then associated with their dechlorination profiles by heterogenous correlation analysis. Our correlation-based visualization approach enables deduction of the roles and functions of bacteria in the dechlorination of chlorinated ethenes. Because it estimates functions and relationships between unidentified microbes and metabolites in microbial ecosystems, this approach is proposed as a control-logic tool by which to understand complex microbial processes.

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Furthermore, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers' family achieves convergence to time-varying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. In addition, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.

Optical microplates for high-throughput screening of photosynthesis in lipid-producing algae

It is well known that biological systems respond to chemical signals as well as physical stimuli. The workhorses of high throughput screening, microplates and pipetting robots, are well suited for screening chemical stimuli; however, there are fewer options for screening physical stimuli, particularly those which involve temporal patterns. This paper presents an optical microplate for photonic high-throughput screening. The system provides addressable intensity and temporal control of LED light emission in each well, and operates on standard black-wall clear-bottom 96-well microplates, which prevent light spillover. Light intensity can be controlled to 7-bit resolution (128 levels), with a maximum intensity of 120 mE cm(-2). The temporal resolution, useful for studying dynamics of light-driven bioprocesses, can be as low as 10 μs. The microplate is used for high-throughput studies of light-dependent growth rates and photosynthetic efficiency in the model organism Dunaliella tertiolecta, a lipid-producing algae of interest in 2(nd) generation biofuels. By conducting 96 experiments in parallel, photoirradiance studies, which would require 2 years using conventional tools, can be completed in <2 weeks. In a 12 day culture, algal growth rates increase with total photon flux, as expected. Interestingly, the lipid production efficiency, defined as lipid production per unit photon flux per capita, increases nearly 5 fold at low light intensity (constant light) and at low duty cycle (pulsed light). High throughput protocols enabled by this system are conducive to systematic studies and discovery in the fields of photobiology and photochemistry.

Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.

Applied advanced process analytics in biopharmaceutical manufacturing: Challenges and prospects in real-time monitoring and control

Biopharmaceutical manufacturing processes are inherently complex due to their nonlinear bioprocess dynamics, variability in batch operations and manufacturing schedule, raw materials involved, and automatic process control. A typical processed lot generates large amounts of data that need to be analyzed and interpreted for process troubleshooting and continuous improvement purposes in addition to product release. Multivariate batch process modeling, monitoring and control approaches in real-time are elaborated by providing industrial examples from the commercial manufacturing processes. Examples and opportunities in cell culture (e.g., bioreactor applications) and purification (e.g., large-scale chromatography) operations are summarized. Impact of process analytical technologies (PAT), soft-sensor development, first principles modeling applications and commercial-scale examples are presented.

Heavy Metals Removal in a Horizontal Rotating Tubular Bioreactor

Mixed microbial culture was isolated from heavy metal-contaminated ground soils located inside iron, vinyl and cement factory area. Isolated mixed microbial culture was used for the heavy metal ions (Fe2+, Cu2+, Ni2+ and Zn2+) removal process in horizontal rotating tubular bioreactor (HRTB). In this research, the effect of bioreactor process parameters on the bioprocess dynamics in the HRTB was studied. Results of this research have shown that profiles of heavy metals concentration were gradually reduced along HRTB at all combinations of bioreactor process parameters [inflow rates (0.5–2.0 L h-1) and rotation speed (5–30 min-1)]. Hydrodynamic conditions and biomass sorption capacity have main impact on the metal ions removal efficiency that was varied in the range of 38.1% to 95.5%. Notable pH gradient (cca 0.7 pH unit) along the HRTB was only observed at the inflow rate of 2.0 L h-1. On the basis of obtained results, it is clear that medium inflow rate (F) has higher impact on the heavy metal removal process than bioreactor rotation speed (n) due to the fact that increase of inflow rate was related to the reduction of equilibrium time for all examined metal ions. Furthermore, equilibrium times for all metal ions are significantly shorter than medium residence times at all examined combinations of bioreactor process parameters. The main impact on the biofilm sorption capacity has covalent index of metal ions and biofilm volumetric density. The sorption capacity of suspended microbial biomass is closely related to its concentration. Results of this research have also shown that the removal of heavy metals ion can be successfully conducted in an HRTB as a one-step process.