Biology subjects

Braatz, R. D.

Publications and source records attributed to Braatz, R. D..

9 recordsLinked to original sources

bioRxiv2025

Thermodynamic Modeling of mRNA with the Addition of Precipitants

Nucleic acid therapeutics (NATs) have recently emerged as an exciting therapeutic modality for a range of indications, most notably as vaccines for SARS-CoV-2. In many cases, the thermodynamics of a system containing nucleic acids (such as in the downstream purification of mRNA from solution or within the lipid nanoparticle) can significantly influence the properties and efficacy of that system. Consequently, an accurate thermodynamic description of the system is essential for understanding and optimizing that system. In this work, the SAFT-{gamma} Mie equation of state was used to predictively model mRNA solubility. Experimental measurements of the solubility of two different mRNA sequences in various conditions (namely choice of precipitant(s), precipitant concentration, and temperature) were obtained and used to validate the model. Not only was the thermodynamic model able to quantitatively predict the solubility of mRNA in solution under different conditions, it was also able to yield mechanistic insight into the factor driving precipitation, namely the disruption of water-mRNA hydrogen bonding. The developed model can be extended to other mRNA sequences in a range of conditions beyond the experimental data presented in this work.

biophysics↗

bioRxiv2025

Continuous Production of Recombinant Adeno-Associated Virus in the Insect Cell/Baculovirus Expression Vector System

Continuous production processes may offer significant advantages for biotherapeutic manufacturing, including increased productivity, consistent product quality, reduced facility footprint, and decreased process turnaround time. Despite these benefits, the in-situ formation of defective recombinant baculovirus expression vectors (BEVs) hinders the continuous manufacturing of recombinant adeno-associated viruses (rAAV) in the baculovirus expression vector system. This study investigates an approach of reducing defective viruses through the infusion of standard recombinant baculovirus (rBV) and the compartmentalization of early- and late-stage infected cells, resulting in stable rAAV production. In this study, rAAVs were continuously produced in a series of cascading reactors, comprising a feeder reactor, an infection reactor, and a production reactor. Residence times and transfer rates across the three reactors were optimized based on the production kinetics of rBV and rAAV derived from our mechanistic model. The majority of rBV was produced within the production reactor, thereby reducing the impact of defective viruses in the infection reactor, enabling continuous rAAV production. This study demonstrates the successful implementation of a continuous rAAV production process, yielding over 5x1010 vg/mL per day for 4 weeks. This work represents the first reported continuous rAAV production process utilizing the Sf9/BEVS platform and establishes engineering know-how for overcoming manufacturing challenges associated with rAAV-based gene therapies.

bioengineering↗

bioRxiv2025

Machine-Learning-Based Prediction of Chinese Hamster Ovary Cell Stability Due to Epigenetic Changes

BackgroundChinese hamster ovary (CHO) cells are the main system for producing recombinant protein biopharmaceuticals, but are inherently unstable, affecting their long-term productivity. This cell instability reduces their productivity over time during perfusion operation, which increases the costs of the resulting biopharmaceutical. No models have been published for the prediction of long-term stability. ResultsIn this work, we create the first models for predicting the long-term stability of CHO cells due to changes in chromatin modification levels and methylation. Multilayer perceptrons are the best-performing models, reaching an F1 score of 59.1% and a Matthews correlation coefficient of 19.4%. The models are successful at identifying stable and highly productive CHO cells. Furthermore, Shapley values and interpretable models are used to investigate model coefficients, contributing biological insight to this problem and helping focus future data collection efforts. The models trained in this work are free and open source and available at github.com/PedroSeber/CHO_stability_prediction, allowing their use in the biopharmaceutical industry, reproduction of this work, and the retraining of models on other datasets. ConclusionsWe show that it is possible to train accurate machine learning models to predict the long-term stability of CHO cells using only epigenetic data. The models have high performance and excel in industrially relevant contexts, and thus can improve the bioproduction of medications, especially recombinant proteins. By providing the first predictive models for this task, this work also serves as a foundation for future data collection and modeling efforts.

bioinformatics↗

bioRxiv2024

Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models

N-glycosylation has many essential biological roles, and is important for biotherapeutics as it can affect drug efficacy, duration of effect, and toxicity. Its importance has motivated the development of mechanistic models for quantitatively predicting the distribution of N-glycans during therapeutic protein production. Here we present a residual hybrid modeling approach that integrates mechanistic modeling with machine learning to produce significantly more accurate predictions for production of monoclonal antibodies in batch, fed-batch, and perfusion cell culture. For the largest dataset, the residual hybrid models have an average 736-fold reduction in testing prediction error. Furthermore, the residual hybrid models have lower prediction errors than the mechanistic models for all of the predicted variables in the datasets. We provide the automatic machine learning software used in this work, allowing other researchers to reproduce this work and use our software for other tasks and datasets.

bioinformatics↗

bioRxiv2024

Efficient Simulation of Viral Transduction and Propagation for Biomanufacturing

Viral transduction is a main route for gene transfer to producer cells in biomanufacturing. Designing a transduction-based biomanufacturing process poses significant challenges, due to the complex dynamics of viral infection and virus-host interaction. This article introduces a software toolkit composed of a multiscale model and an efficient numeric technique that can be leveraged for determining genetic and process designs that optimize transduction-based biomanufacturing platforms. Viral transduction and propagation for up to two viruses simultaneously can be simulated through the model, considering viruses in either lytic or lysogenic stage, during batch, perfusion, or continuous operation. The model estimates the distribution of the viral genome(s) copy number in the cell population, which is an indicator of transduction efficiency and viral genome stability. The infection age distribution of the infected cells is also calculated, indicating how many cells are in an infection stage compatible with recombinant product expression and/or with viral amplification. The model can also consider the presence in the system of defective interfering particles, which can severely compromise the productivity of biomanufacturing processes. Model benchmarking and validation are demonstrated for case studies on the baculovirus expression vector system and influenza A propagation in suspension cultures. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/587435v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1752f59org.highwire.dtl.DTLVardef@779987org.highwire.dtl.DTLVardef@8d14org.highwire.dtl.DTLVardef@2e4514_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Recurrent Neural Network-based Prediction of O-GlcNAcylation Sites in Mammalian Proteins

O-GlcNAcylation has the potential to be an important target for therapeutics, but a motif or an algorithm to reliably predict O-GlcNAcylation sites is not available. In spite of the importance of O-GlcNAcylation, current predictive models are insufficient as they fail to generalize, and many are no longer available. This article constructs MLP and RNN models to predict the presence of O-GlcNAcylation sites based on protein sequences. Multiple different datasets are evaluated separately and assessed in terms of strengths and issues. The models trained in this work achieve considerably better metrics than previously published models, with at least a two-fold increase in F1 score relative to previously published models; the specific gains vary depending on the dataset. Within a given dataset, the results are robust to changes in cross-validation and test data as determined by nested validation. The best model achieves an F1 score of 36% (more than 3.5-fold greater than the previous best model) and a Matthews Correlation Coefficient of 35% (more than 4.5-fold greater than the previous best model), and, for the F1 score, 7.6-fold higher than when not using any model. Shapley values are used to interpret the model s predictions and provide biological insight into O-GlcNAcylation.

bioinformatics↗

bioRxiv2023

Linear and Neural Network Models for Predicting N-glycosylation in Chinese Hamster Ovary Cells Based on B4GALT Levels

Glycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, predictive models have been lacking. This article constructs linear and neural network models for the prediction of the distribution of glycans on N-glycosylation sites. The models are trained on data containing normalized B4GALT levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 1.39%, which is 10-fold smaller than for previously published models, and a narrow error distribution. We also discuss issues with other models reported in the literature. We provide all of the software used in this work, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.

bioinformatics↗

bioRxiv2023

Mechanistic Modeling Explains the Production Dynamics of Recombinant Adeno-Associated Virus with the Baculovirus Expression Vector System

The demand for recombinant adeno-associated virus (rAAV) for gene therapy is expected to soon exceed current manufacturing capabilities, considering the expanding number of approved products and of pre-clinical and clinical stage studies. Current rAAV manufacturing processes have less-than-desired yields and produce a significant amount of empty capsids. Recently, FDA approved the first rAAV-based gene therapy product manufactured in the baculovirus expression vector system (BEVS). The BEVS technology, based on an invertebrate cell line derived from Spodoptera frugiperda, demonstrated scalable production of high volumetric titers of full capsids. In this work, we develop a mechanistic model describing the key extracellular and intracellular phenomena occurring during baculovirus infection and rAAV virion maturation in the BEVS. The predictions of the model show good agreement with experimental measurements reported in the literature on rAAV manufacturing in the BEVS, including for TwoBac, ThreeBac, and OneBac constructs. The model is successfully validated against measured concentrations of structural and non-structural protein components, and of vector genome. We carry out a model-based analysis of the process, to provide insights on potential bottlenecks that limit the formation of full capsids. The analysis suggests that vector genome amplification is the limiting step for rAAV production in TwoBac. In turn, vector genome amplification is limited by low Rep78 levels. For ThreeBac, low vector genome amplification dictated by Rep78 limitation appears even more severe than in TwoBac. Transgene expression in the insect cell during rAAV manufacturing is also found to negatively influence the final rAAV production yields.

molecular biology↗

bioRxiv2021

Weighing the DNA content of Adeno-Associated Virus vectors with zeptogram precision using nanomechanical resonators

Quantifying the composition of viral vectors used in vaccine development and gene therapy is critical for assessing their functionality. Adeno-Associated Virus (AAV) vectors, which are the most widely used viral vectors for in-vivo gene therapy, are typically characterized using PCR, ELISA, and Analytical Ultracentrifugation which require laborious protocols or hours of turnaround time. Emerging methods such as Charge-Detection Mass Spectroscopy, Static Light Scattering, and Mass Photometry offer turnaround times of minutes for measuring AAV mass, but mostly require purified AAV-based reference materials for calibration. Here, we demonstrate a method for using Suspended Nanomechanical Resonators (SNR) to directly measure both AAV mass and aggregation from a few microliters of sample within minutes. We achieve a resolution near 10 zeptograms which corresponds to 1% of the genome holding capacity of the AAV capsid. Our results show the potential of our method for providing real-time quality control of viral vectors during biomanufacturing.

bioengineering↗