Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Abstract

Genome-scale metabolic models describe cellular metabolism with mechanistic detail.Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been wellstudied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells -including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines

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Optimizing VLP production in gene therapy: Opportunities and challenges for in silico modeling

“…Although their model was not specifically tailored to the cell line clone (neither cell-specific model nor cell-specific biomass composition), it was a promising example of how GSMMs facilitated process optimization and avoided time-consuming DoEs. Even though artificial biomass reactions are commonly used for growth simulation as an objective function, [46] this concept might be questionable: Schinn et al [81] showed much more accurate growth predictions when using reactive oxygen species (reactive oxygen species (ROS)) synthesis or mitochondrial reduced nicotinamide-adenine dinucleotide (reduced nicotinamide-adenine dinucleotide (NADH)) regeneration as objective function for example. This aspect needs to be studied in more detail since selecting an appropriate objective function is one of the most fundamental pillars of the simulation of GSMMs.…”

How reliable are Chinese hamster ovary (CHO) cell genome‐scale metabolic models?

“…While some attempts have been made to measure CHO cell GEM predictive performance (Schinn et al, 2021b ; Széliová, 2021a , 2021b ), the effectiveness of CHO cell GEMs at capturing both intracellular flux distributions and extracellular phenotypes has not been comprehensively assessed. In this paper we present an evaluation of extracellular and intracellular predictive capabilities of CHO cell GEMs, using a holistic 13 C‐labeled fluxomic assessment methodology.…”