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1. Discrete Feature Representations of CHO Reaction Mechanisms as Quasireaction Subgraphs

   https://doi.org/10.5281/zenodo.7905294  

   Rappoport (January 2023, Zenodo)

   {"Abstract":["This data set contains 194778 quasireaction subgraphs extracted from CHO transition networks with 2-6 non-hydrogen atoms (CxHyOz, 2 <= x + z <= 6).<\/p>\n\nThe complete table of subgraphs (including file locations) is in CHO-6-atoms-subgraphs.csv file. The subgraphs are in GraphML format (http://graphml.graphdrawing.org) and are compressed using bzip2. All subgraphs are undirected and unweighted. The reactant and product nodes (initial and final) are labeled in the "type" node attribute. The nodes are represented as multi-molecule SMILES strings. The edges are labeled by the reaction rules in SMARTS representation. The forward and backward reading of the SMARTS string should be considered equivalent.<\/p>\n\nThe generation and analysis of this data set is described in\nD. Rappoport, Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models, 2023, submitted. Preprint at ChemrXiv, DOI: 10.26434/chemrxiv-2023-wltcr<\/p>\n\nSimulation parameters\n- CHO networks constructed using polar bond break/bond formation rule set for CHO.\n- High-energy nodes were excluded using the following rules:\n  (i) more than 3 rings, (ii) triple and allene bonds in rings, (iii) double bonds at\n  bridge atoms,(iv) double bonds in fused 3-membered rings.\n- Neutral nodes were defined as containing only neutral molecules.\n- Shortest path lengths were determined for all pairs of neutral nodes.\n- Pairs of neutral nodes with shortest-path length > 8 were excluded.\n- Additionally, pairs of neutral nodes connected only by shortest paths passing through\n  additional neutral nodes (reducible paths) were excluded.<\/p>\n\nFor background and additional details, see paper above.<\/p>"],"Other":["This work was supported in part by the National Science Foundation under Grant No. CHE-2227112."]} 

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2. An investigation of nucleotide sugar dynamics under the galactose supplementation in CHO cell culture

   https://doi.org/10.1016/j.procbio.2019.03.020  

   Sha, Sha; Yoon, Seongkyu (June 2019, Process Biochemistry)
3. Systematic identification of safe harbor regions in the CHO genome through a comprehensive epigenome analysis

   https://doi.org/10.1002/bit.27599  

   Hilliard, William; Lee, Kelvin_H (October 2020, Biotechnology and Bioengineering)

   Abstract The Chinese hamster ovary (CHO) cell lines that are used to produce commercial quantities of therapeutic proteins commonly exhibit a decrease in productivity over time in culture, a phenomenon termed production instability. Random integration of the transgenes encoding the protein of interest into locations in the CHO genome that are vulnerable to genetic and epigenetic instability often causes production instability through copy number loss and silencing of expression. Several recent publications have shown that these cell line development challenges can be overcome by using site‐specific integration (SSI) technology to insert the transgenes at genomic loci, often called “hotspots,” that are transcriptionally permissive and have enhanced stability relative to the rest of the genome. However, extensive characterization of the CHO epigenome is needed to identify hotspots that maintain their desirable epigenetic properties in an industrial bioprocess environment and maximize transcription from a single integrated transgene copy. To this end, the epigenomes and transcriptomes of two distantly related cell lines, an industrially relevant monoclonal antibody‐producing cell line and its parental CHO‐K1 host, were characterized using high throughput chromosome conformation capture and RNAseq to analyze changes in the epigenome that occur during cell line development and associated changes in system‐wide gene expression. In total, 10.9% of the CHO genome contained transcriptionally permissive three‐dimensional chromatin structures with enhanced genetic and epigenetic stability relative to the rest of the genome. These safe harbor regions also showed good agreement with published CHO epigenome data, demonstrating that this method was suitable for finding genomic regions with epigenetic markers of active and stable gene expression. These regions significantly reduce the genomic search space when looking for CHO hotspots with widespread applicability and can guide future studies with the goal of maximizing the potential of SSI technology in industrial production CHO cell lines. 

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4. High‐density adherent culture of CHO cells using rolled scaffold bioreactor

   https://doi.org/10.1002/bit.28079  

   YekrangSafakar, Ashkan; Mehrnezhad, Ali; Wu, Tongyao; Park, Kidong (March 2022, Biotechnology and Bioengineering)
5. Text Mining of CHO Cell Bibliome: Topic Modeling and Document Classification

   Wang, Qinghua; Olshin, Jonathan; Vijay-Shanker, K.; Wu, Cathy (August 2022, bioRxiv)

   Chinese hamster ovary (CHO) cells are widely used for mass production of therapeutic proteins in the pharmaceutical industry. With the growing need in optimizing the performance of producer CHO cell lines, research on CHO cell line development and bioprocess continues to increase in recent decades. Bibliographic mapping and classification of relevant research studies will be essential for identifying research gaps and trends in literature. To qualitatively and quantitatively understand the CHO literature, we have conducted topic modeling using a CHO bioprocess bibliome manually compiled in 2016, and compared the topics uncovered by the Latent Dirichlet Allocation (LDA) models with the human labels of the CHO bibliome. The results show a significant overlap between the manually selected categories and computationally generated topics, and reveal the machine-generated topic-specific characteristics. To identify relevant CHO bioprocessing papers from new scientific literature, we have developed a supervised learning model, Logistic Regression, to identify specific article topics and evaluated the results using three CHO bibliome datasets, Bioprocessing set, Glycosylation set, and Phenotype set. The use of top terms as features supports the explainability of document classification results to yield insights on new CHO bioprocessing papers. 

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