Metabolic engineering of Bcat1, Adh5 and Hahdb towards controlling metabolic inhibitors and improving performance in CHO cell-cultures

Kuang, Bingyu; Hoang, Duc; Fu, Qiang; Park, SoYoung; Liang, George; Yoon, Seongkyu

doi:10.1016/j.bej.2024.109282

Biochemical Engineering Journal

2024

DOI: 10.1016/j.bej.2024.109282

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Metabolic engineering of Bcat1, Adh5 and Hahdb towards controlling metabolic inhibitors and improving performance in CHO cell-cultures

Bingyu Kuang,

Duc Hoang,

Qiang Fu

et al.

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2024

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Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

Liang,

Sha,

Wang

et al. 2024

Front. Mol. Biosci.

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Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models.

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Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

Liang,

Sha,

Wang

et al. 2024

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

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Metabolic engineering of Bcat1, Adh5 and Hahdb towards controlling metabolic inhibitors and improving performance in CHO cell-cultures

Cited by 1 publication

References 33 publications

Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

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