Bioprocess Optimization Research Articles

Oxygen transfer and gas holdup in airlift bioreactors assembled with helical flow promoters.

Bioreactors can perform biochemical conversions mediated by biocatalysts, such as enzymes, animal cells, plants, and microorganisms. Among several existing models, airlift bioreactors are devices with the low shear environment and good mass transfer with low energy consumption, employed in several biochemical processes. The fluid flow is enabled through air injection by the sparger located at the bioreactor base. Despite its simple geometry compared with the conventional bioreactors, airlift performance can be optimized via geometrical modifications. Therefore, the objective of this work was to evaluate the effects of the addition of helical flow promoters, positioned in the riser and/or downcomer regions of an airlift of concentric tubes measuring the volumetric oxygen coefficient (kLa) and gas holdup. The results obtained by varying the gas flow rate from 1.0 to 4.0 vvm allowed the system evaluation of oxygen transfer and gas holdup. The inclusion of helical flow promoters increased the kLa, reaching up to 23% in oxygen transfer compared to tests without helicoids and up to 14% increase in the gas holdup. The inclusion of helical flow promotors was beneficial for all gas flow rates. Thus, including these flow promoters is an effective strategy to increase the oxygen transfer rate for bioprocess optimization.

Bioprocess development and optimization of parameters for enhancing bio-assisted method for sulfur removal from lakhra coal using rhodococcus sp SL-9

ABSTRACT The burning of coal releases toxic gases that disturb the environmental conditions. It is imperative to use this energy as much as possible. Due to its characteristics, it is possible to manipulate unused loss reserves. In this case, it is necessary to continue research emphasizing clean coal technology from an energy perspective, safety, and environmental sustainability problem. It wants to be implemented that 50% of the world’s coal reserves are low grades. In this regard, the current work is related to removing sulfur obtained from coal using newly isolated bacteria sl-9. It was determined that different process parameters might be used to remove Sulfur. There is a reduction of 45% of the total Sulfur in unprocessed coal when several parameters are controlled, such as 20% (w/v) pulp density, 180 rpm, 20 mesh (850 microns), and pH 7. So, Sl-9 has the potential to lower coal’s sulfate content.

Utilization of high-K+-cane molasses for enhanced S-Adenosylmethionine production by manipulation of a K+ transport channel in Saccharomyces cerevisiae

S-Adenosylmethionine (SAM) is a valuable compound extensively used in depression treatment and biological methyl donor, which is biosynthesized from ATP and L-methionine with SAM synthase (SAM-s) as a catalyst. In this research, the weak SAM-s activity was a key factor and led to low SAM titer in Saccharomyces cerevisiae. Then, a novel K+ uptake channel was introduced to the SAM production pathway of Saccharomyces cerevisiae, activated by K+ in molasses to enhance the SAM synthase (SAM-s) activity and thereby improve the production efficiency of SAM. As a result, the intracellular K+ concentration and SAM-s activity were 1.23- and 1.31-fold higher than the control leading to a 24 % increase to 0.93 ± 0.04 g/L in SAM titer. In combination with bioprocess optimization resulted in a final yield of 2.22 ± 0.01 g/L in shake flasks and 15.37 ± 0.89 g/L in a 5-L bioreactor from pretreated molasses. This work provides a practical and cost-effective strategy for the large-scale biosynthesis of SAM from cane molasses.

From Microbial Succinic Acid Production to Polybutylene Bio‐Succinate Synthesis

AbstractA simplified and scalable one‐pot process for the anaerobic production of succinic acid using a metabolically engineered Corynebacterium glutamicum strain is demonstrated. With targeted bioprocess optimization, succinic acid titer of 78 g L−1 and yield of 1.41 molSAmolGLC−1 were achieved. Succinic acid was recovered from the neutral fermentation broth by electrochemically induced crystallization and applied for polybutylene bio‐succinate synthesis using a biocompatible zinc catalyst. Except for a slight color change, the final biopolymer was comparable to the polymer from commercial precursors.

Volatile Fatty Acids Production by Acidogenic Fermentation of Wastewater: A Bibliometric Analysis

This study presents a bibliometric analysis of the scientific literature on volatile fatty acids (VFA) production from wastewater fermentation published from 1981 to 21 June 2021. A total of 618 papers obtained from the Scopus database were analyzed using VOSviewer 1.6.16 software. According to the results, this topic has been capturing the attention of researchers over the years, but with different research approaches, including optimization of anaerobic digestion in two-stage reactors, biological removal of nutrients from wastewater, energy production in bioelectrochemical systems, and recovery of VFA as value-added intermediate products to be used as inputs in a variety of industries. In addition, the bibliometric networks obtained from the authors’ keyword frequency showed that wastewater treatment by using fermentation to obtain VFA as a value-added by-product is an emerging topic that undoubtedly requires further research and collaboration between scientific institutions. In this regard, different types of wastewater have been used as a substrate for acidogenic fermentation; however, and based on the results, the production of VFA from cassava processing wastewater is seen as one of the emerging issues of this field. Finally, evaluating the effect of operating conditions on the fermentation process, such as pH, hydraulic retention time, organic loading rate, temperature, inoculum and substrate concentration, independent of the final application of the VFA produced, is a relevant aspect for bioprocess optimization and implementation on a large scale.

Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures.

Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems.

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.

Steps toward a digital twin for functional food production with increased health benefits

Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin.Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses.The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion.Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.

Towards advanced bioprocess optimization: A multiscale modelling approach

Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.

Photobiological production of high-value pigments via compartmentalized co-cultures using Ca-alginate hydrogels

Engineered cyanobacterium Synechococcus elongatus can use light and CO2 to produce sucrose, making it a promising candidate for use in co-cultures with heterotrophic workhorses. However, this process is challenged by the mutual stresses generated from the multispecies microbial culture. Here we demonstrate an ecosystem where S. elongatus is freely grown in a photo-bioreactor (PBR) containing an engineered heterotrophic workhorse (either β-carotene-producing Yarrowia lipolytica or indigoidine-producing Pseudomonas putida) encapsulated in calcium-alginate hydrogel beads. The encapsulation prevents growth interference, allowing the cyanobacterial culture to produce high sucrose concentrations enabling the production of indigoidine and β-carotene in the heterotroph. Our experimental PBRs yielded an indigoidine titer of 7.5 g/L hydrogel and a β-carotene titer of 1.3 g/L hydrogel, amounts 15–22-fold higher than in a comparable co-culture without encapsulation. Moreover, 13C-metabolite analysis and protein overexpression tests indicated that the hydrogel beads provided a favorable microenvironment where the cell metabolism inside the hydrogel was comparable to that in a free culture. Finally, the heterotroph-containing hydrogels were easily harvested and dissolved by EDTA for product recovery, while the cyanobacterial culture itself could be reused for the next batch of immobilized heterotrophs. This co-cultivation and hydrogel encapsulation system is a successful demonstration of bioprocess optimization under photobioreactor conditions.

Biotechnology: use of available carbon sources on the planet to generate alternatives energy

Biotechnology has been an essential tool in the search for solutions and in the optimization of bioprocesses associated with issues of human, plant, animal, energy and also the balance of ecosystems on planet Earth. The objective of this research was to present an unconventional substrate (cellulose), in abundance on the planet, to be used as a substitute source of carbon and energy for biotechnology processes, with the possibility of increasing industrial production of biomass and energy. As basis for the research, an extensive literature review and quantitative and qualitative analyzes were carried out. Genetic Engineering techniques were used to enable the yeast Saccharomyces cerevisiae for partial cellulose degradation, through the use of genetic transformation methods to insert a plasmid carrying the cellobiohydrolase cDNA. It was found that the recombinant and biologically active cellobiohydrolase protein was expressed and excreted in haploid and diploid laboratory yeast strains. The analyzes allowed the visualization of cellulolysis halos around colonies of recombinant strains grown in solid YPD medium with 1% microgranular cellulose. The recombinant clones derived from the haploid lineage yielded in average of 1.70 mg ART/mL, while recombinant clones derived from the diploid lineage produced in average of 2.05 mg ART/mL.

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.

Non-invasive real-time monitoring of cell concentration and viability using Doppler ultrasound.

Bioprocess optimization towards higher productivity and better quality control relies on real-time process monitoring tools to measure process and culture parameters. Cell concentration and viability are among the most important parameters to be monitored during bioreactor operations that are typically determined using optical methods on an extracted sample. In this paper, we have developed an online non-invasive sensor to measure cell concentration and viability based on Doppler ultrasound. An ultrasound transducer is mounted outside the bioreactor vessel and emits a high frequency tone burst (15 MHz) through the vessel wall. Acoustic backscatter from cells in the bioreactor depends on cell concentration and viability. The backscattered signal is collected through the same transducer and analyzed using multivariate data analysis (MVDA) to characterize and predict the cell culture properties. We have developed accurate MVDA models to predict the Chinese hamster ovary (CHO) cell concentration in a broad range from 0.1×106 cells/mL to 100×106 cells/mL, and cell viability from 3% to 99%. The non-invasive monitoring is ideal for single use bioreactor and the in-situ measurements removes the burden for offline sampling and dilution steps. This method can be similarly applied to other suspension cell culture modalities.

Bioprocess optimization platform for valorization of poly(lactic)-based bioplastic waste using PLA-degrading actinobacteria, Saccharothrix sp. MY1 cultured in silk wastewater as low-cost nutrient source

Bioprocess optimization platform for valorization of poly(lactic)-based bioplastic waste using PLA-degrading actinobacteria, Saccharothrix sp. MY1 cultured in silk wastewater as low-cost nutrient source

The Potential Application of Single‐Use Bioreactors in Cultured Meat Production

AbstractProduction of cultured meat by proliferation of stem cells is a fast‐growing field drawing significant attention and big investments. Replacement of traditional meat by cell‐based meat can have a huge positive impact on the environmental burden of human meat consumption. Large‐scale production to make cell‐based meat economically feasible and comparable to the cost of conventional meat requires further bioprocess optimization and large investments. Application of single‐use bioreactors at a restricted scale of operation reduces the overall capital expenditures for cultured meat production. The advantages of single‐use bioreactors as experienced in biopharmaceutical applications are applicable to cultured meat production. Here, it is shown that the transfer from traditional cultivation systems to single‐use systems is easily possible. Single‐use bioreactors can contribute to the realization of cultured meat production at “farm level”, thus limiting the investment and technical risks of very large‐scale productions.

Exploration of a novel and efficient source for production of bacterial nanocellulose, bioprocess optimization and characterization

The demand for bacterial nanocellulose is expected to rise in the coming years due to its wide usability in many applications. Hence, there is a continuing need to screen soil samples from various sources to isolate a strain with a high capacity for bacterial nanocellulose production. Bacillus sp. strain SEE-12, which was isolated from a soil sample collected from Barhiem, Menoufia governorate, Egypt, displayed high BNC production under submerged fermentation. Bacillus sp. strain SEE-12 was identified as Bacillus tequilensis strain SEE-12. In static cultures, BNC was obtained as a layer grown in the air liquid interface of the fermentation medium. The response surface methodology was used to optimise the process parameters. The highest BNC production (22.8 g/L) was obtained using 5 g/L peptone, 5 g/L yeast extract, 50%, v/v Cantaloupe juice, 5 g/L Na2HPO4, 1.5 g/L citric acid, pH 5, medium volume of 100 mL/250 mL conical flask, inoculum size 5%, v/v, temperature 37 °C and incubation time 6 days. The BNC was purified and characterized by scanning electron microscopy (SEM), Energy-dispersive X-ray (EDX) spectroscopy, differential scanning calorimetry (DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and transmission electron microscopy (TEM).

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 II: probabilistic quantification of carboxylic acid reductase activity for bioprocess optimization

Autonomously operated parallelized mL-scale bioreactors are considered the key to reduce bioprocess development cost and time. However, their application is often limited to products with very simple analytics. In this study, we investigated enhanced protein expression conditions of a carboxyl reductase from Nocardia otitidiscaviarum in E. coli. Cells were produced with exponential feeding in a L-scale bioreactor. After the desired cell density for protein expression was reached, the cells were automatically transferred to 48 mL-scale bioreactors operated by a liquid handling station where protein expression studies were conducted. During protein expression, the feed rate and the inducer concentration was varied. At the end of the protein expression phase, the enzymatic activity was estimated by performing automated whole-cell biotransformations in a deep-well-plate. The results were analyzed with hierarchical Bayesian modelling methods to account for the biomass growth during the biotransformation, biomass interference on the subsequent product assay, and to predict absolute and specific enzyme activities at optimal expression conditions. Lower feed rates seemed to be beneficial for high specific and absolute activities. At the optimal investigated expression conditions an activity of 1153 U mL^{-1} was estimated with a 90% credible interval of [992, 1321] U mL^{-1}. This is about 40-fold higher than the highest published data for the enzyme under investigation. With the proposed setup, 192 protein expression conditions were studied during four experimental runs with minimal manual workload, showing the reliability and potential of automated and digitalized bioreactor systems.

Editorial: Bioprocess optimization: The potential of agro-food by-products

Editorial: Bioprocess optimization: The potential of agro-food by-products

Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization.

To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.