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Dynamic metabolic models of CHO cell cultures through minimal sets of elementary flux modes

► Study of detailed metabolic networks of the different life phases of CHO-320 cells. ► Minimal decomposition in elementary flux modes. ► Fast derivation of macroscopic bioreactions and maximum reaction rates. ► Piecewise dynamic models of the culture evolution. The concept of Elementary Flux Modes...

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Bibliographic Details
Published in:Journal of biotechnology 2013-04, Vol.164 (3), p.409-422
Main Authors: Zamorano, F., Vande Wouwer, A., Jungers, R.M., Bastin, G.
Format: Article
Language:English
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Summary:► Study of detailed metabolic networks of the different life phases of CHO-320 cells. ► Minimal decomposition in elementary flux modes. ► Fast derivation of macroscopic bioreactions and maximum reaction rates. ► Piecewise dynamic models of the culture evolution. The concept of Elementary Flux Modes (EFMs) has been of central importance in a number of studies involving the analysis of metabolism. In Provost and Bastin (2007) this concept is used to translate the metabolic networks of the different phases of CHO cell cultures into macroscopic bioreactions linking extracellular substrates to products. However, a critical issue concerns the calculation of these elementary flux vectors, as their number combinatorially increases with the size of the metabolic network. In this study, a detailed metabolic network of CHO cells is considered, where the above-mentioned combinatorial explosion makes the computation of the elementary flux modes impossible. To alleviate this problem, a methodology proposed in Jungers et al. (2011) is used to compute a decomposition of admissible flux vectors in a minimal number of elementary flux modes without explicitly enumerating all of them. As a result, a set of macroscopic bioreactions linking the extracellular measured species is obtained at a very low computational expense. The procedure is repeated for the several cell culture phases and a global model is built using a multi-model approach, which is able to successfully predict the evolution of experimental data.
ISSN:0168-1656
1873-4863
DOI:10.1016/j.jbiotec.2012.05.005