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

Abstract

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.

Exploring metabolic responses and pathway changes in CHO‐K1 cells under varied aeration conditions and copper supplementations using 1H NMR‐based metabolomics

“…The affected pathways are not independent of each other but rather interconnected. [ 42 ] Metabolites such as histidine, phenylalanine, tyrosine, and tryptophan, found within these metabolic pathways, could serve as novel target nutrients to enhance culture performance. These amino acids, vital for cell growth and productivity in other CHO cell lines, warrant further investigation to ascertain their role in the cell line utilized in this research.…”

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

“…[ 83 ] The most recent version of CHO‐GSMM [ 32 ] was generated by combining previous models. By comparing the updated global CHO‐GSMM, two cell line specific CHO‐GSMMs and a recently published reduced CHO‐model, [ 84 ] it was shown that the reduced models predict core metabolic functions more precisely, whereas essential gene identification is more accurate in the cell line specific models. [ 32 ] Despite the comprehensive comparison, the impact of selecting the most suitable objective function for phenotype prediction remains to be investigated.…”

Advanced cell technologies: Making protein, cell, and gene therapies a reality

“…An important contribution to the field of cell line and metabolic engineering is provided by Strain et al (2023) with a review on the current state of CHO genome‐scale metabolic models (GEM), their inability to model intracellular metabolism and capture extracellular phenotypes. In addition, Jiménez del Val et al (2023) presented an improved GEM, iCHO2441, as well as two cell line‐specific GEMs for CHO‐S and CHO‐K1 that may serve as foundations for better design and assess next‐generation flux analysis techniques. Jiménez del Val et al (2023) propose a more compact network model, CHOmpact, that can provide improved interpretations from simulations, including the identification of shifts in key metabolic behaviors.…”

“…In addition, Jiménez del Val et al (2023) presented an improved GEM, iCHO2441, as well as two cell line‐specific GEMs for CHO‐S and CHO‐K1 that may serve as foundations for better design and assess next‐generation flux analysis techniques. Jiménez del Val et al (2023) propose a more compact network model, CHOmpact, that can provide improved interpretations from simulations, including the identification of shifts in key metabolic behaviors. This model could also serve as a platform for dynamic models used for process control and optimization.…”