Approach toward an efficient inoculum preparation stage for suspension BHK-21 cell culture

Núñez, Eutímio Gustavo Fernández; Leme, Jaci; Parizotto, Letícia de Almeida; Rezende, Alexandre Gonçalves de; Costa, Bruno Labate Vale da; Boldorini, Vera Lucia Lopes; Jorge, Soraia Attie Calil; Astray, Renato Mancini; Pereira, Carlos Augusto; Caricati, Celso Pereira; Tonso, Aldo

doi:10.1007/s10616-014-9756-6

Cited by 6 publications

(3 citation statements)

References 22 publications

(29 reference statements)

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“…4A-C). For a time of infection of 72 h, no virus production was detected; this finding could be justified by the nutrient limitations in the culture medium (end of growth phase) [21] and relative low temperature for this cell line (<37 • C), which hinder virus attachment and replication. The highest multiplicity of infection (0.1) led to the highest virus production values at 34 • C in all the assessed conditions.…”

Section: Resultsmentioning

confidence: 85%

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Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data

Takahashi

Rocha

Núñez

2016

Process Biochemistry

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This work objective was to define a modeling approach based on genetic algorithm (GA) for optimizing parameters of an artificial neural network (ANN); the latter describes rabies virus production in BHK-21 cells based on empirical data derived from uniform designs (UDs) with different numbers of experimental runs. The parameters considered for viral infection were temperature (34 and 37 • C), multiplicity of infection (0.04, 0.07, and 0.1), infection, and harvest times (24, 48, and 72 h), with virus production as the monitored output variable. A multilevel factorial experimental design was performed and used to train, validate, and test the ANN. Its experimental fractions (18, 24, 30, 36, and 42 runs) defined by UDs were used to simulate the neural architectures. In GA, the neural computing parameters constituted the population individuals, and the steps involved were population creation, selection, and replacement by crossover and mutation. The ANN optimized by the combined algorithm showed a good calibration for all UDs under consideration, thus demonstrating to be suitable (R > 0.85) as a correlation method in UDs independent of the experimental runs developed. Therefore, this work could guide researchers in the efficient use of UDs in the simulation and optimization of virus production processes.

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Section: Resultsmentioning

confidence: 85%

“…Cell samples from spinner flask at 24, 48, and 72 h post inoculation were withdrawn for viral infection experiments; the cell concentrations at each moment were 0.75 × 10 6 , 2 × 10 6 , and 4 × 10 6 cell/mL, respectively. The cell viability was 100% at the three sampling moments under consideration [21].…”

Section: Cell Culturementioning

confidence: 99%

Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data

Takahashi

Rocha

Núñez

2016

Process Biochemistry

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“…In the static method, cells generally accumulate on the culture surface of the T-flask [17] , leading to a high local cell density and insufficient oxygen and nutrient supplies to the cells. As a result, the cell proliferation rate decreases.…”

Section: Discussionmentioning

confidence: 99%

Suspension culture in a T-flask with acoustic flow induced by ultrasonic irradiation

Fujii

Kurashina

Terao

et al. 2021

Ultrasonics Sonochemistry

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Use of uniform designs in combination with neural networks for viral infection process development

Buenno¹,

Rocha²,

Leme

et al. 2015

Biotechnology Progress

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This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.

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Approach toward an efficient inoculum preparation stage for suspension BHK-21 cell culture

Cited by 6 publications

References 22 publications

Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data

Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data

Suspension culture in a T-flask with acoustic flow induced by ultrasonic irradiation

Use of uniform designs in combination with neural networks for viral infection process development

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