Responses of GS-NS0 Myeloma cells to osmolality: Cell growth, intracellular mass metabolism, energy metabolism, and antibody production

Zhao, Liang; Fan, Li; Wang, Jiaqi; Niu, Hongxing; Tan, Wen‐Song

doi:10.1007/s12257-008-0223-0

Cited by 12 publications

(5 citation statements)

References 28 publications

(22 reference statements)

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“…Specifically, the growth functions ( r i ) are glutamate-dependent ( Equation 7 ). This dependency was introduced since it has been reported that glutamine, or glutamate in the GS system [ 50 ], is the main energy-yielding metabolic route [ 11 , 51 , 52 ] with over 90% flowing into the TCA cycle [ 53 ]. In addition, the cyclin growth functions ( r i = G1,G2 ) were also formulated as lactate-dependent since it has been reported [ 54 , 55 ] that its concentration inhibits cell growth.…”

Section: Resultsmentioning

confidence: 99%

“…The biomass yield on glutamate and glucose is represented by ( Y i = Glu,Glc , Equation 15 ). Lactate was assumed to be produced ( Y Lac , Equation 16 ) by both glucose and glutamate consumption, as previously reported [ 11 , 12 , 51 , 53 ]. In addition, lactate is also consumed ( Q Lac ) [ 14 , 55 ], which in this case was modelled and triggered by the depletion of glutamate.…”

Section: Resultsmentioning

confidence: 99%

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Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

et al. 2015

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Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins—key molecules that regulate cell cycle transition—have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model’s use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

et al. 2015

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“…The concentrations of glucose, ammonia, and lactate were determined using test kits purchased from Nanjing Jiancheng Bioengineering Institute and Shanghai Institute of Biological products, China. The specific consumption/production rate (q) was calculated by plotting cumulative metabolites consumption versus the integral of viable cells (IVC) (Zhao et al 2009), and fitting the plots by a liner correlation. The slopes of the corresponding straight lines were used as average specific rates.…”

Section: Analytical Measurements and Specific Metabolic Ratesmentioning

confidence: 99%

“…To better control cell metabolism and avoid using of animal-derived raw materials and ill-defined hydrolysates, much progress has been made in understanding cell nutrient requirements and development of chemically defined medium including feed formulations over the last 10 years (Wurm 2004;Franek et al 2000;Huang et al 2010;Young 2013). More recently, metabolic flux analysis (MFA) has become a useful tool in medium development for hybridoma Europa et al 2000;Gambhir et al 2003) and CHO cell cultures (Nyberg et al 1999;Altamirano et al 2006), which can help quantifying nutrient demands and metabolite production in cell culture (Zhao et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Responses of CHO-DHFR cells to ratio of asparagine to glutamine in feed media: cell growth, antibody production, metabolic waste, glutamate, and energy metabolism

Zhang

Wang

et al. 2016

Bioresour. Bioprocess.

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Background: Optimization of chemically defined medium has been a critical way to produce monoclonal antibody. Usually, amount of glutamine was added into the feed medium, but a half of asparagine was added. Our study found that asparagine was important in the antibody production phase. Increasing the ratio of asparagine to glutamine in feed medium for enhancement of antibody production in CHO-DHFR cell culture would be an efficient way. Results:We optimized the total amount and the ratio of the two vital amino acids in feed medium to increase antibody production. In this work, we have demonstrated that feeding medium of high ratio between asparagine and glutamine (FB-H) can enhance the cell density after reaching the stationary phase. Moreover, FB-H was shown to improve cell maintenance, and increased the antibody production. The metabolic flux analysis proved that ratio of asparagine to glutamine had little influence on glycolysis. Furthermore, the TCA cycle of FB-H was enhanced by 20 % compared to that of low ratio of asparagine to glutamine (FB-L). And the energy metabolism of FB-H was 22.6 % higher than that of FB-L. For the later, lactate can be less produced in FB-H. Conclusions:We should improve the ratio between asparagine and glutamine in feed medium properly under the premise of no influence on cell growth to achieve high mAb producing goal.

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“…The [11,12,51,53]. In addition, lactate is also consumed (Q Lac ) [14,55], which in this case was modelled and triggered by the depletion of glutamate.…”

Section: Cyclins Growth Functionsmentioning

confidence: 99%

A Cyclin Distributed Cell Cycle Model in GS-NS0

Munzer

Kostoglou

Georgiadis

et al. 2014

Computer Aided Chemical Engineering

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Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins-key molecules that regulate cell cycle transitionhave been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cyclespecific production rates. The solution method delivered fast computational time that renders the model's use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.PLOS Computational Biology |

show abstract

Responses of GS-NS0 Myeloma cells to osmolality: Cell growth, intracellular mass metabolism, energy metabolism, and antibody production

Cited by 12 publications

References 28 publications

Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

Responses of CHO-DHFR cells to ratio of asparagine to glutamine in feed media: cell growth, antibody production, metabolic waste, glutamate, and energy metabolism

A Cyclin Distributed Cell Cycle Model in GS-NS0

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