(237d) Modelling Amino Acid Metabolism in Mammalian Cells - Towards the Development of a Model Library

Amino acids have been experimentally identified as key factors of animal cell cultures, with a demonstrated effect on cell growth and protein productivity (Duval et al., 1991). However, with the exception of certain single-cell models (Sanderson, 1999), they have largely been excluded from mathematical representations of these processes, possibly due to lack of widely available relevant measurements. Such large mathematical models may be more detailed representations of the underlying biological system (see Sidoli et al., 2004, for a comprehensive review), but have so far had limited applications, as they are computationally demanding and require complex intracellular measurements in order to be validated.

Unstructured models, on the other hand, can provide a useful alternative for the development of process systems applications. A model that successfully predicts experimental measurements of extracellular variables, such as cell and nutrient concentrations, is often sufficient for the development of control and optimisation strategies and as a process design tool (Pörtner and Schäfer, 1996; Iyer et al., 1998, Dowd et al., 1999). Attempts to model mammalian culture systems have also primarily focused on one cell line at a time, even though various commonly used cell lines carry similar characteristics.

In this work, we demonstrate that a single model structure can be used to describe the cell growth kinetics and metabolism of human embryonic kidney (HEK-293) and Chinese hamster ovaries (CHO-IFNγ) cells. The proposed unstructured model has been developed according to the metabolic network of each cell line, which was determined through exhibited experimental behaviour and various literature sources (Stryer, 1995; KEGG). The mathematical formulation comprises 51 differential and algebraic equations and uses Monod kinetics to describe the metabolism of 19 amino acids and glucose. In both cases the model simulation results exhibit good agreement with experimental data from batch and fed-batch cultures of the two cell lines. These findings will hopefully help overcome the notion of limited extrapolating ability of unstructured models, as applicability of the proposed model to a wide range of operating conditions is demonstrated.

The proposed model can therefore be used for further exploration of cell biology through experimental design and for the development of control and optimisation strategies for cell culture processes. Eventually, it can be enriched with structured information on cell metabolism, protein production, etc., as this is deciphered and as advances in computational power permit its exploitation. Overall, this study demonstrates that there is scope for the development of a model library for commonly used cell lines, aiming at reaping the advantages of model-based approaches widely used in other engineering applications.

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