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Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis
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Orientador(es)
Resumo(s)
Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.
Descrição
Funding Information: This work was sponsored by GlaxoSmithKline Biologicals SA whereby the NOVA University Lisbon was engaged under an Agreement for R and D Services. All authors were involved in the conception and design of the study. PD’s lab performed the experiments/acquired the data. JR, GO, RO analyzed and interpreted the data. All authors were involved in drafting the manuscript or critically revising it for important intellectual content. All authors had full access to the data and approved the manuscript before it was submitted by the corresponding author. Publisher Copyright: © 2022, The Author(s).
Palavras-chave
CHO-K1 cells Culture media design Flux balance analysis Genome-scale modeling Hybrid semi-parametric systems Machine learning Biotechnology Bioengineering