scholarly journals A genome‐scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures

2021 ◽  
Vol 118 (5) ◽  
pp. 2118-2123
Author(s):  
Song‐Min Schinn ◽  
Carly Morrison ◽  
Wei Wei ◽  
Lin Zhang ◽  
Nathan E. Lewis
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Metabolic Network ◽  
Cell Cultures ◽  
Chinese Hamster Ovary ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
A Genome ◽  
Genome Scale ◽  
Amino Acid Concentrations

scholarly journals A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures

2020 ◽  
Author(s):  
Song-Min Schinn ◽  
Carly Morrison ◽  
Wei Wei ◽  
Lin Zhang ◽  
Nathan E. Lewis
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Large Scale ◽  
Time Course ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
A Genome ◽  
Metabolic Models ◽  
Genome Scale ◽  
Amino Acid Concentrations

AbstractThe control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations. This potential has not been realized due to unresolved challenges: (1) the models assume an optimal and highly efficient metabolism, and therefore tend to underestimate amino acid consumption, and (2) the models assume a steady state, and therefore have a short forecast range. We address these challenges by integrating machine learning with the metabolic models. Through this we demonstrate accurate and time-course dependent prediction of individual amino acid concentration in culture medium throughout the production process. Thus, these models can be deployed to control nutrient feeding to avoid premature nutrient depletion or provide early predictions of failed bioreactor runs.

Identification and quantitation of vesivirus 2117 particles in bioreactor fluids from infected Chinese hamster ovary cell cultures

2012 ◽  
Vol 110 (5) ◽  
pp. 1342-1353 ◽  
Author(s):  
Yongchang Qiu ◽  
Nathan Jones ◽  
Michelle Busch ◽  
Peng Pan ◽  
Jesse Keegan ◽  
...  
Keyword(s):  
Chinese Hamster Ovary Cell ◽  
Cell Cultures ◽  
Chinese Hamster Ovary ◽  
Chinese Hamster ◽  
Ovary Cell

Membrane potential and amino acid transport in a mutant chinese hamster ovary cell line

1991 ◽  
Vol 146 (3) ◽  
pp. 417-424 ◽  
Author(s):  
Bianca Maria Rotoli ◽  
Ovidio Bussolati ◽  
Valeria Dall'asta ◽  
Gian Carlo Gazzola
Keyword(s):  
Membrane Potential ◽  
Amino Acid ◽  
Cell Line ◽  
Chinese Hamster Ovary Cell ◽  
Chinese Hamster Ovary ◽  
Amino Acid Transport ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
Acid Transport ◽  
Ovary Cell Line

scholarly journals Amino Acid Requirements of the Chinese Hamster Ovary Cell Metabolism during Recombinant Protein Production

2019 ◽  
Author(s):  
Bergthor Traustason
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Recombinant Protein ◽  
Recombinant Proteins ◽  
Chinese Hamster Ovary ◽  
Cho Cells ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
Scale Model ◽  
Amino Acid Requirements

SummaryMajority of biopharmaceutical drugs today are produced by Chinese hamster ovary (CHO) cells, which have been the standard industry host for the past decades. To produce and secrete a substantial amount of the target recombinant proteins the CHO cells must be provided with suitable growth conditions and provided with the necessary nutrients. Amino acids play a key role in this as the building blocks of proteins, playing important roles in a large number of metabolic pathways and being important sources of nitrogen as well as carbon under certain conditions. In this study exploratory analysis of the amino acid requirements of CHO cells was carried out using metabolic modelling approaches. Flux balance analysis was employed to evaluate the optimal distribution of fluxes in a genome-scale model of CHO cells to gain information on the cells’ metabolic response in silico.The results showed that providing non-essential amino acids (NEAAs) has a positive effect on CHO cell biomass production and that cysteine as well as tyrosine play a fundamental role in this. This implies that extracellular provision of NEAAs limits the extent of energy loss in amino acid biosynthetic pathways and renders additional reducing power available for other biological processes. Detailed analysis of the possible secretion and uptake of D-serine in the CHO model was also performed and its influence on the rest of the metabolism mapped out, which revealed results matching various existing literature. This is interesting since no mention of D-serine in regard to CHO cells was found in current literature, as well as the fact that this opens up the possibility of using the model for better understanding of certain disorders in higher up organisms that have been implicated with D-serine, such as motor neuron and cognitive degeneration. Finally, outcome from the model optimisation of different recombinant proteins demonstrated clearly how the difference in protein structure and size can influence the production outcome. These results show that systematic and model-based approaches have great potential for broad de novo exploration as well as being able to handle the cellular burden associated with the production of different types of recombinant protein.

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