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

Fouladiha, Hamideh; Marashi, Sayed-Amir; Li, Shangzhong; Li, Zerong; Masson, Helen O.; Vaziri, Behrouz; Lewis, Nathan E.

doi:10.1101/2020.01.27.921296

Cited by 7 publications

(5 citation statements)

References 63 publications

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“…[ 152,153 ] However, the gap‐filling process is vital for developing an accurate and predictive reconstruction. [ 154 ] ML methods such as ensemble methods have shown improvements in the refinements of genome‐scale metabolic reconstructions. [ 155 ] Third, although very accurate, the results of the integrated models are not necessarily appropriate for large‐scale industrial fermentations.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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Section: Challenges and Perspectivesmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

Self Cite

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“…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

et al. 2021

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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 parsimonious flux balance analysis (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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“…The inclusion of gene-protein reaction associations provided a direct link between genes and metabolic reactions. Since then, significant expansions and improvements to iCHO1766 have been achieved, such as gap-filling studies that also removed dead-end reactions (Fouladiha et al, 2021), and the integration of a core protein secretory pathway, iCHO2048, enabling the computation of energetic costs and machinery demands of each secreted protein (Gutierrez et al, 2020). Interestingly, iCHO2048 was subsequently used to direct host cell protein knockout studies which resulted in increased recombinant protein productivity and a cleaner feedstock for downstream processing steps (Kol et al, 2020), highlighting the power these models hold for identifying diverse cellular engineering strategies.…”

Section: Introductionmentioning

confidence: 99%

CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Val

Kyriakopoulos

Albrecht

et al. 2021

Preprint

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Metabolic modelling has emerged as a key tool for the characterisation of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimise the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are, therefore, difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy multi-objective optimisation and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and, with further development, will serve as a platform to develop dynamic metabolic models for the control and optimisation of biopharmaceutical cell culture processes.

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

Cited by 7 publications

References 63 publications

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

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