Application of Dynamic Metabolic Flux Convex Analysis to CHO-DXB11 Cell Fed-batch Cultures

Fernandes, Sofia Afonso; Robitaille, Julien; Bastin, Georges; Jolicœur, Mario; Wouwer, Alain Vande

doi:10.1016/j.ifacol.2016.07.386

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…Glycolysis accounts for 45% of the ATP generated in the cell during the exponential growth phase, 23 resulting in, when combined with mitochondrial ATP export, higher cytosolic concentrations of ATP than the mitochondria. 24,25 Consistent with the Warburg Effect, the CHO cells have the highest glucose uptake rate 7,26 and glycolytic flux 4,23,25,[27][28][29] through glucose-6-phosphate isomerase (GPI) during the exponential growth phase compared to other growth phases. Elevated lactate dehydrogenase (LDH) flux is generally observed in cells demonstrating the Warburg effect, as this regenerates the NAD + from NADH necessary for glycolysis.…”

Section: Trends In Glycolysismentioning

confidence: 82%

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed‐batch conditions

Rish

Drennen

Anderson

2021

Biotechnology Progress

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Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.

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Section: Trends In Glycolysismentioning

confidence: 82%

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed‐batch conditions

Rish

Drennen

Anderson

2021

Biotechnology Progress

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“…The network used in this work has been largely inspired from Fernandes et al (2016) and adapted to take all available measurements into account. It contains r = 82 reactions among which 27 are transport reactions, m = 51 internal metabolites and p = 27 external metabolites measured in the extracellular medium ( CO 2 is not measured), and is presented in Table 1 .…”

Section: Network Presentationmentioning

confidence: 99%

“…The biomass synthesis reaction is also borrowed from the metabolic network of Fernandes et al (2016) which is designed for CHO cells, and not HEK cells. Nevertheless, Pe'er et al (2004) proves that the average amino acid composition of eukaryote cells is very similar from one strain to another.…”

Section: Network Presentationmentioning

confidence: 99%

“…In addition to traditional MPA methods, extreme admissible values of individual internal fluxes can be determined through computation of flux variability analysis (FVA) which consists in solving a simple linear programming problem ( Leighty and Antoniewicz, 2011;Llaneras et al, 2012;Vercammen et al, 2014 ). While the usual analysis provides an insight into the cell activity under specific growth conditions, in the course of the exponential phase or the stationary phase for example, several studies provide a dynamic overview over the whole experiment using FBA ( Mahadevan et al, 2002 ), MPA ( Fernandes et al, 2016;Zamorano et al, 2013 ) or FVA ( Richelle et al, 2016;Bogaerts et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive flux variability analysis of HEK cell cultures

Abbate

Dewasme

Wouwer

et al. 2020

Computers & Chemical Engineering

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Measured external fluxes impose constraints to under-determined metabolic networks that narrow the internal flux intervals obtained using Flux Variability Analysis. Nevertheless, these constraints often lead to systems that do not admit a feasible solution. Measurement noise and data smoothing are among the sources of uncertainties that can cause system infeasibility. These constraints are classically released using interval representation of fluxes. This study investigates the use of Adaptive Flux Variability Analysis (AFVA), which allows determining a minimal coefficient of variation of the external fluxes along the time course of the experiment. Especially, AFVA is applied to a medium-size metabolic network and a rich dataset relative to HEK-293 cells cultured in batch, encompassing all 20 amino acids and less commonly measured metabolites, such as urea and pyruvate. AFVA appears as an effective tool for metabolic flux analysis. The impact of data-smoothing and the information provided by the cell growth are thoroughly analyzed.

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“…Kamp and Klamt (2017) use EFV to test the feasibility of growth-coupled production of five metabolites for a small network of the core metabolism of E. coli. EFV has also been used to determine bounds for internal fluxes in a dynamic metabolic flux analysis (DMFA) that approximated time intervals to a pseudo steady state (FERNANDES et al, 2016). De Groot et al (2019 show that, for metabolisms aiming optimal growth in growth-limiting situations, only a small number of EFM are active pathways or, equivalently, only one EFV.…”

Section: Elementary Flux Vectorsmentioning

confidence: 99%

Desenvolvimentos de ferramentas matemáticas para modelagem de sistemas biológicos baseados em fluxos metabólicos e estimação de parâmetros de problemas mal condicionados.

Nakama¹

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Mathematical modeling is one of the basis of metabolic engineering, guiding genetic modifications through the study of metabolic fluxes. Stoichiometric models are an important tool to analyze metabolic networks, especially for non-model organisms or during initial analysis, since they linear models and essentially require the stoichiometry matrix and information on reversibility of the reactions as input. They can be used to explore different assumptions and scenarios, and elucidate some properties of the metabolism. Therefore some stoichiometric modeling techniques were implemented in a single stand-alone software and used to study the core metabolism of the bacteria Burkholderia sacchari for polyhydroxyalkanoate production, showing that they are a valuable tool for exploring how metabolisms work and guiding future experiment design. However, cellular metabolism is actually subjected to nonlinear dynamics and, therefore, nonlinear models are better suited to represent more diverse physiological states, which can result in better predictions. Mechanistic models are a class of such models; however, in the metabolic engineering context, all frameworks that have been proposed to estimate the kinetic parameters involved are prone to identifiability issues. Based on this obstacle, an investigation on regularization methods for ill-conditioned parameter estimation problems was conducted. Regularization methods based on the eigenvalue decomposition of the (reduced) Hessian matrix were shown to be optimal for linear parameter estimation, in the sense of reducing parameter variance, and helpful in dealing with nonlinear problems with nearly flat neighborhood around the solution. Moreover, the eigenvector-based regularization in both cases was able to recognize groups of correlated parameters, which allows for better understanding the underlying identifiability issues.

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Application of Dynamic Metabolic Flux Convex Analysis to CHO-DXB11 Cell Fed-batch Cultures

Cited by 5 publications

References 22 publications

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed‐batch conditions

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed‐batch conditions

Adaptive flux variability analysis of HEK cell cultures

Desenvolvimentos de ferramentas matemáticas para modelagem de sistemas biológicos baseados em fluxos metabólicos e estimação de parâmetros de problemas mal condicionados.

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