Hagrot E, Oddsdóttir HÆ, Mäkinen M, Forsgren A, Chotteau V. Novel column generation-based optimization approach for poly-pathway kinetic model applied to CHO cell culture.
Metab Eng Commun 2018;
8:e00083. [PMID:
30809468 PMCID:
PMC6376161 DOI:
10.1016/j.mec.2018.e00083]
[Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 10/30/2018] [Accepted: 12/08/2018] [Indexed: 11/26/2022] Open
Abstract
Mathematical modelling can provide precious tools for bioprocess simulation, prediction, control and optimization of mammalian cell-based cultures. In this paper we present a novel method to generate kinetic models of such cultures, rendering complex metabolic networks in a poly-pathway kinetic model. The model is based on subsets of elementary flux modes (EFMs) to generate macro-reactions. Thanks to our column generation-based optimization algorithm, the experimental data are used to identify the EFMs, which are relevant to the data. Here the systematic enumeration of all the EFMs is eliminated and a network including a large number of reactions can be considered. In particular, the poly-pathway model can simulate multiple metabolic behaviors in response to changes in the culture conditions.
We apply the method to a network of 126 metabolic reactions describing cultures of antibody-producing Chinese hamster ovary cells, and generate a poly-pathway model that simulates multiple experimental conditions obtained in response to variations in amino acid availability. A good fit between simulated and experimental data is obtained, rendering the variations in the growth, product, and metabolite uptake/secretion rates. The intracellular reaction fluxes simulated by the model are explored, linking variations in metabolic behavior to adaptations of the intracellular metabolism.
Novel method to model multiple states by a poly-pathway kinetic model.
EFMs relevant to data identified by column generation (CG)-based optimization.
CG optimization enables use of networks much larger than systematic enumeration.
A kinetic model simulates changes in metabolic rates linked to available amino acids.
The flux distribution of each metabolic state is visualized in the original network.
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