Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome scale metabolic models (GSMM) and by assuming instantaneous pseudo steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry (MS) coupled with liquid (LCMS) and gas chromatography (GCMS) for a fed-batch, high cell density cultivation of E. coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in TCA cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

In this paper, Long Short-Term Memory (LSTM) networks and multilayered feedforward neural networks (FFNNs) were combined with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system. The adaptive moment estimation method (ADAM) with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network (DNN) architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed hybrid FFNN models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with 7 internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R 2=0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.

The aim of this study was to investigate the survival, distribution and reaction of different cell types on a monolayer disk, as well as their behavior under bioreactor treatment. Specifically, porcine EEC and porcine fibroblasts (PCF) were labeled with GFT and Texas Red, respectively, to track their viability and distribution. The experiments involved monitoring the cells using various microscopy techniques and comparing the results with controls. These findings have important implications for understanding cell behavior and potential applications for Discrete Subaortic Stenosis. This paper aims to discuss the implications of the findings in the context of existing literature and future research directions.

Bioreactor scale-up is complicated by dynamic interactions between mixing, reaction, mass transfer, and biological phenomena, the effects of which are usually predicted with simple correlations or case-specific simulations. This two-part study investigated whether axial diffusion equations could be used to calculate mixing times and to model and characterize large-scale stirred bioreactors in a general and predictive manner without fitting the diffusivity parameter. In this first part, a resistances-in-series model analogous to basic heat transfer theory was developed to estimate the diffusivity such that only available hydrodynamic numbers and literature data were needed in calculations. For model validation, over 800 previously published experimentally determined mixing times were predicted with the transient axial diffusion equation. The collected data covered reactor sizes up to 160 m 3, single- and multi-impeller configurations, aerated and non-aerated operation in turbulent and transition flow regimes, and various mixing time quantification methods. The model performed excellently for typical multi-impeller configurations as long as flooding conditions were avoided. Mixing times for single-impeller and few non-standard bioreactors were not predicted equally well. The transient diffusion equation together with the developed transfer resistance analogy proved to be a convenient and predictive model of mixing in typical large-scale bioreactors.

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, and CO 2 were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Design (QbD) approach, it’s critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with off-line analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the Micro Sequential Injection (µSI) process analyzer and the Automatic Assay Preparation Platform (A2P2) system. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an off-line analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a GMP environment.

Enzymes that catalyze post-translational modifications of peptides and proteins (PTM-enzymes) – proteases, protein ligases, oxidoreductases, kinases, and other transferases - are foundational to our understanding of health and disease and empower applications in chemical biology, synthetic biology, and biomedicine. To fully harness the potential of PTM-enzymes, there is a critical need to decipher their enzymatic and biological mechanisms, develop molecules that can probe and reprogram them, and endow them with improved and novel functions. These objectives are contingent upon implementation of high-throughput functional screens and selections that interrogate large sequence libraries to isolate desired PTM-enzyme properties. This review discusses the principles of S. cerevisiae organelle sequestration to study and engineer PTM-enzymes. These include methods that modify yeast surface display and employ enzyme-mediated transcription activation to evolve the activity and substrate specificity of proteases and protein ligases. We also present a detailed discussion of yeast endoplasmic reticulum (ER) sequestration for the first time. Where appropriate, we highlight the major features and limitations of different systems, specifically how they can measure and control enzyme catalytic efficiencies. Taken together, yeast-based high-throughput sequestration approaches significantly lower the barrier to understanding how PTM-enzymes function and how to reprogram them.

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modelling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Lastly, we identify standing challenges in protein-constraint metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

Biomaterials are important tools for the reconstruction of organs and tissues, and silica is widely used in these reconstruction technologies. Hence, a lexical and content analysis was carried out on articles on application of silica in biomaterials, based on a search in the Capes’ Journals Portal using the keywords “silica, biomedical, application, scaffold”, between the years 2009 and 2019. The 110 selected articles were analyzed using the IRaMuTeQ software, Word Cloud and Descending Hierarchical Classification (DHC). The words: “silica, cell, nanosilica, bone, material, scaffold and application” appear prominently in the Word Cloud and DHC indicating four classes: (1) physical characterization, (2) biomedicine applications, (3) engineering applications and (4) compatibility characterization. Thus, the analysis of DHC and Word Cloud showed that the main ways of using silica are: mesoporous silica nanoparticles, amorphous silica, silica-based materials, nanofibers and silica hybrids, and the main biomaterials developed are scaffolds, grafts, aerogels, hydrogels, membranes and drug delivery systems.

Targeted gene knockdown has become one of the most powerful tools in molecular biology and holds substantial promise in therapeutic applications. While existing technologies such as siRNAs, CRISPRi, and ASOs effectively and specifically reduce gene expression, few can be used to first discover the genes that influence a particular phenotype and then directly transition to being used as oligonucleotide therapeutics. Thus, a tool that could help bridge the gap between target discovery and the development of therapeutic leads would benefit the scientific community. Here, we present hnRNPA1 recruiting oligonucleotides, or AROs, as single-stranded RNA (ssRNA) molecules that knockdown transcript levels of target genes. AROs target specific pre-mRNA transcripts via sequence homology and leverage the ubiquitous and abundant endogenous RNA-binding protein hnRNPA1 to degrade target transcripts. Using RT-qPCR, we show that AROs effectively knock down target genes when delivered via a plasmid and expressed using a Pol II promoter or when delivered directly as single-strand RNAs. Additionally, as proof of principle, we use a ssRNA ARO to knockdown KRT14 in squamous cell carcinoma and show reduced invasive potential. We believe AROs fill an important niche in the scientific toolbox by taking advantage of endogenous RNA binding machinery for RNA knockdowns.

Abstract Generally, investigations on nanomedicine involve conventional imaging techniques for obtaining static images on nanoparticle internalization at a single time point where various phases can be overlooked. In contrast, 3D live-cell imaging can be used for obtaining cellular retention of drugs at various phases, and cells can be followed for days. This article demonstrates the application of time-lapse microscopy in the investigation of Poly-L-lysine coated ZnO nanoparticle dynamics. In this work, a laser scanning confocal microscope has been employed to quantify the dynamics of internalization particles and reactive oxygen species generation (ROS) using volumetric imaging. Firstly, we show that simultaneous spatial mapping of nanoparticle uptake in MCF-7 cells and ROS in a single cell can be used to identify the interdependence between the accumulation of particles and ROS generation. Secondly, monitoring of ROS formation and cytotoxicity using the same imaging platform offers an advantage over monitoring these parameters using various instruments. Finally, the ability of the fluorescent particles in inducing a significant reduction in cell viability suggests its potential to be used as a therapeutic agent. The proposed framework opens up a new avenue of research for investigating mechanistic aspects of ZnO particle adsorption in vitro through long term imaging. Keywords: Fluorescent ZnO particle, Time-lapse microscopy, 3D Live-cell imaging, laser scanning confocal microscope, Reactive oxygen species

The global pandemic outbreak, SARS-COV-2, which causes COVID-19, has coerced numerous pharmaceutical companies to sprint for the vaccine and therapeutic biologics development. Most of the therapeutic biologics are common human IgG antibodies, which were identified by next-generation sequencing with the B cells from the convalescent patients in less than one-month post-infection. While the global public health emergency calls for medications urgently, it saves lives to expedite the clinical trials of biologics as much as possible, hence the biologics development strategies are unprecedentedly challenged. Since the advent of therapeutic biologics, transfection, and selection strategy has been continuously improving for developing more robust cell lines with greater productivity and efficiency. Next-generation sequencing (NGS) has also been implemented into cell bank testing for acceleration. These recent advances enable us to rethink and reshape the chemistry, manufacturing and controls (CMC) strategy against the pandemic outbreaks, to start supplying cGMP materials for the life-saving clinical trials as soon as possible. We elucidated an accelerated CMC workflow for biologics against pandemics, including using cGMP-compliant pool materials for Phase I clinical trials, selecting the final clone with similar product quality as Phase I materials for late-stage development and commercial production and matching product quality among different manufacturing stages.

In modeling cell culture growth using unstructured model, two types of equations are normally used: logistic and Monod. However, these two equations are known for their limitations to model death phase of cell culture growth and to account for dead cells accumulation data. In this paper, we present a modeling framework whereby both Logistic and Monod equations can be used in a single set of equations system to overcome these limitations. First, it can be shown that the increase of total cell population that consists of viable and dead cells follows a logistic growth pattern with its own intrinsic growth rate and total carrying capacity. Furthermore, a hybrid Logistic-Monod equation with first-order decay kinetics can be used to model viable cell growth data with decline phase effectively. With this paradigm, a pseudo-rate equation can be written to account for dead cells accumulation data using population balancing with a simple understanding that dead cell population is simply the difference between total and viable cells. These equations can be adjoined with substrate consumption and product generation rate equations to depict complete batch growth data that covers exponential growth and death phases. This modeling framework has been fitted successfully to fit batch growth data of two cell lines from published literature with complete depictions of dead cell accumulation and cell viability profiles. The implication of this modeling framework for chemostat culture performance analysis is further investigated. 

Plant biomass constitutes the main resource of renewable carbon in the planet and its valorization has traditionally been focused on the use of cellulose, although hemicellulose is the second most abundant group of polysaccharides on earth. Enzymes involved in its degradation are usually glycosyl hydrolases and filamentous fungi are good producers of these enzymes. In this study, a new strain of Aspergillus niger was utilized for hemicellulase production under solid state fermentation using wheat straw as a single carbon source. Physicochemical parameters for production of an endoxylanase were optimized by using one factor at a time approach and response surface methodology (RSM). Maximum xylanase yield after RSM optimization was increased 3-fold. The enzyme was purified by ultrafiltration and ion-exchange chromatography1.41-fold, with 6.2 % yield. Highest xylanase activity was observed at 50 °C and pH 6. A high pH and thermal stability were found, greater than 90% residual activity between pH 3.0-9.0 and between 30-40°C, after 24 h of incubation, presenting half-lives of 30 min at 50 and 60°C. Enzyme was mostly active for wheat arabinoxylan, and displayed the following kinetic parameters Km of 26.06 mg•ml-1 and Vmax of 5,647 U•mg-1min-1.Wheat straw xylan hydrolysis with the purified β-1,4 endoxylanase showed that it was able to release xylooligosaccharides, making it suitable for different applications in food technology.

Steviol glycosides, mostly extracted from Stevia rebaudiana Bertoni, are non-caloric, safe natural sweeteners. Steviosides make up more than 50% of total steviol glycosides but possess a bitter aftertaste, which limits their application as food additives. Trace amounts of rubusosides are acceptable sweeteners, but their supply is limited. In this study, we screened an endophytic bacteria (Lelliottia LST-1) that can specifically convert stevioside into rubusoside and reach a conversion rate of 75.4% after response surface optimization. Potential β-glucosidases GH3-1, GH3-2, GH3-3, and GH3-4 based on whole genome sequencing were expressed and interacted with steviol glycosides to explore principle and process of biological transformation. High-performance liquid chromatography revealed that all enzymes hydrolyzed stevioside and generated rubusoside, but substrate specificity analysis indicated that GH3-2 is the most suitable choice for the durative, steady, and high-efficiency production of rubusoside

Fed-batch culture currently represents the typical choice for the production of monoclonal antibodies (mAbs) in the biopharmaceutical industry. However, the implementation of perfusion culture process combined with continuous manufacturing has gained attention due to increased productivity and resource savings. In this paper, we compared the host cell protein (HCP) production and profile of mAb1 between fed-batch and perfusion culture processes. Our work demonstrated differences in HCP production based on the type of cell culture process for the first time. We focused on HCPs that get carried through the purification process and are present in the final drug substance at levels impacting antibody quality and stability. Perfusion process had lower HCP levels and enabled higher clearance of problematic HCPs compared to fed-batch suggesting a viable alternative process. Furthermore, our work demonstrates proof of concept of the impact of cell culture process on specific product quality and help to navigate the process design when we move from traditional fed-batch to next-generation perfusion cell culture.

Chinese hamster ovary (CHO) is the most commonly used host cell line for therapeutic protein production. Their exposure to highly concentrated feed solution during fed-batch cultivation can cause an unphysiological osmolality increase (>300 mOsm/kg) affecting cell physiology, morphology, and proteome. In a companion article “Hyperosmolality in CHO Culture: Effects on Cellular Behavior and Morphology” we show that hyperosmolalities of up to 545 mOsm/kg force cells to ablate proliferation and gradually increase their volume, almost triplicating it. CHO cells also exhibit a significant hyperosmolality-dependent mitochondrial activity increase. To get a deeper insight into molecular mechanisms involved in these processes, we performed a comparative quantitative label-free proteome study of hyperosmolality-exposed vs. control CHO cells. Our analysis revealed key differentially expressed proteins mediating mitochondrial activation, oxidative stress amelioration, and cell cycle progression. We also discovered a previously unknown strong regulation of proteins altering cell membrane rigidity and permeability. Among others, we detected three members of septins, filamentous proteins forming diffusion barriers in the cell, to be highly upregulated in response to hyperosmolality. Taken together, our observations correlate well with the recent CHO-based fluxome and transcriptome studies and expose new unknown targets involved in response to hyperosmotic pressure in mammalian cells.

293T suspensions cells were first cultured in a big bubble-stirred bioreactors for large-scale lentivirus vectors (LV) production in the study. Direct domesticated approaches were utilized for 293 suspensions cells. We validated of the ability to LV package and drew a growth curve in shake flask. The results show 293T suspension culture was successful, and also retained the capacity for LV production. After facile optimization of the viral package and cryopreservation conditions, 293T suspension cells were inoculated in a 5.5 L bioreactor. Packaged CAR-CD30 lentivirus yield was close to that from the original shake flask culture. Cultivation for 4 days in the bioreactor enabled production of 1.5±0.3×107 TU/mL coarse LV and the lentiviral infection efficiency was 48.6±2.8% in T Cells. The process has merits of low cost as well as more effective, and expected to be highly competitive compared to other bioreactors.