Model-Based Control of Hybridoma Cell Cultures

Hass, Volker C.; Lane, Phil; Hoffmann, Martin; Frahm, Björn; Schwabe, J. O.; Pörtner, Ralf; Munack, Axel

doi:10.1016/s1474-6670(17)34240-4

Cited by 2 publications

(2 citation statements)

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“…Model-based methods are applied in the context of model-assisted Design of Experiments, the design, layout and optimization of production processes [ 19 , 20 , 28 , 31 , 32 , 36 , 38 , 44 ]. Furthermore, they are used as predictive models, enabling prediction of the process future, featuring the development of decision making, optimization and control strategies [ 9 , 11 , 29 , 36 ]. The performance mainly depends on prediction accuracy of the model which in turn depends on data quality [ 39 ], the complexity of the model and the ability to address batch-to-batch variabilities [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…concerning cell passaging [ 8 , 14 , 18 ] or to control the process, e.g. through an Open-Loop-Optimal-Control (OLFO) method for the control of optimal feeding [ 11 , 26 ] or to control pH and temperature shifts [ 35 ]. As soon as new process data become available model updating, meaning re-estimation of model parameters over a growing estimation horizon (window), is performed (= dynamic parameter estimation).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Rodríguez

Posch

Pörtner

et al. 2020

Bioprocess Biosyst Eng

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Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Rodríguez

Posch

Pörtner

et al. 2020

Bioprocess Biosyst Eng

View full text Add to dashboard Cite

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Macroscopic modeling of mammalian cell growth and metabolism

Yahia

Malphettes

Heinzle

2015

Appl Microbiol Biotechnol

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We review major modeling strategies and methods to understand and simulate the macroscopic behavior of mammalian cells. These strategies comprise two important steps: the first step is to identify stoichiometric relationships for the cultured cells connecting the extracellular inputs and outputs. In a second step, macroscopic kinetic models are introduced. These relationships together with bioreactor and metabolite balances provide a complete description of a system in the form of a set of differential equations. These can be used for the simulation of cell culture performance and further for optimization of production.

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Model-Based Control of Hybridoma Cell Cultures

Cited by 2 publications

References 1 publication

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Macroscopic modeling of mammalian cell growth and metabolism

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