Abstract
Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.
Highlights
Batch cultivation is one of the most commonly used operation modes for animal cell culture–based production of high‐value biopharmaceuticals, that is, recombinant proteins and vaccines
Note that the simulations cover the time period after achieving the maximum cell concentration starting at about 130 hr after inoculation, which is often neglected in cell growth models, for instance in previous studies dealing with the same cell line (Borchers et al, 2013)
The experimental data is rather noisy, the same trend is observed for all cultivations, as it is typically observed for suspension cell line cultivations
Summary
Batch cultivation is one of the most commonly used operation modes for animal cell culture–based production of high‐value biopharmaceuticals, that is, recombinant proteins and vaccines. Conventional DoE approaches cannot explicitly consider the intracellular dynamics of animal cells, that is, crucial aspects of the central energy and carbohydrate metabolism Another option for process optimization and design is the well‐ known constraint‐based modeling. Dynamic models should be used to allow for the handling of complex and high‐dimensional experimental data This is achieved with the simulation of changes (such as metabolite concentration) over the time course using a set of ordinary differential equations (ODEs) with defined initial conditions and simplified, but biologically valid assumptions regarding cell growth, product formation, and enzyme kinetics (Bailey, 1998; Batt & Kompala, 1989). The model addresses options for improving cell growth and measures toward the establishment of a more efficient metabolism
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