Enzyme capacity-based genome scale modelling of CHO cells

Yeo, Hock Chuan; Hong, Jongkwang; Lakshmanan, Meiyappan; Lee, Dong-Yup

doi:10.1016/j.ymben.2020.04.005

Cited by 58 publications

(54 citation statements)

References 53 publications

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“…Therefore, the applicability of CHO model in bioprocess studies can be increased by refining the metabolic models. Recently, an extended version of GEM of CHO cells was released, in which new constraints were added to the model based on enzyme capacity of the reactions (52). Yet, the focus of our study is to fill the gaps and manually curate the previous model (22).…”

Section: Discussionmentioning

confidence: 99%

Systematically gap-filling the genome-scale metabolic model of CHO cells

Fouladiha

Marashi

et al. 2020

Preprint

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ObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In comparison to other mammalian cell types, in vitro handling of CHO cells is relatively easy. For example, CHO cells can grow in suspension and reach high cellular densities in bioreactors. Therefore, studying the metabolism of CHO cells to improve the bio-production of these cells is an important subject of research. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism.ResultsHere, we expanded the existing model of Chinese hamster metabolism (iCHO1766) with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. This updated model substantially increased the number of reactions capable of carrying flux.ConclusionsThe addition of these new data provides an important step towards more complete metabolic models of CHO cells.

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Section: Discussionmentioning

confidence: 99%

Systematically gap-filling the genome-scale metabolic model of CHO cells

Fouladiha

Marashi

et al. 2020

Preprint

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“…For example, Lularevic et al [55] reduced variability in flux variability analysis by adding carbon availability constraints. In another study, the predictions of intracellular fluxes were improved by adding constraints based on enzyme kinetic information [9]. This also lead to a correct prediction of the overflow metabolism (the secretion of lactate).…”

Section: Discussionmentioning

confidence: 99%

“…In 2016, a community-derived, consensus genome-scale metabolic model (GSMM) of CHO was published [8] and several updates have been made since [9][10][11]. These serve as a basis for applying genome-scale metabolic modeling to CHO.…”

Section: Introductionmentioning

confidence: 99%

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Széliová

Štor

Thiel

et al. 2020

Preprint

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Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by pFBA using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

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“…This modelling paradigm has been further established in recent years by the development of theoretical and computational extensions based on CBMs principles. Following the contributions to develop dFBA based on both static and dynamic optimization methods [40,141], efforts also focused on the integration of transcriptional regulation [230,231], and the consideration of resource allocations in terms of enzyme production cost [12,232]. Particularly in systems metabolic engineering of strains, rewarding applications of MPA methods fueled continual development of up-to-date mathematical modelling frameworks [233,234], recently finding optimal operating points in two-stage bioprocesses [235].…”

Section: Perspectives and Challenges Aheadmentioning

confidence: 99%

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Yasemi¹,

Jolicœur²

2021

Processes

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Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

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Enzyme capacity-based genome scale modelling of CHO cells

Cited by 58 publications

References 53 publications

Systematically gap-filling the genome-scale metabolic model of CHO cells

Systematically gap-filling the genome-scale metabolic model of CHO cells

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

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