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Genome-scale Models As a Vehicle for Knowledge Transfer from Microbial to Mammalian Cell Systems

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Specialty Biotechnology
Date 2023 Mar 7
PMID 36879884
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Abstract

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

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References
1.
Zhuang K, Vemuri G, Mahadevan R . Economics of membrane occupancy and respiro-fermentation. Mol Syst Biol. 2011; 7:500. PMC: 3159977. DOI: 10.1038/msb.2011.34. View

2.
Jensen P, Papin J . Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics. 2010; 27(4):541-7. PMC: 6276961. DOI: 10.1093/bioinformatics/btq702. View

3.
Pan S, Reed J . Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. Curr Opin Biotechnol. 2017; 51:103-108. DOI: 10.1016/j.copbio.2017.12.012. View

4.
Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T . Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput Biol. 2012; 8(7):e1002575. PMC: 3390398. DOI: 10.1371/journal.pcbi.1002575. View

5.
Lerman J, Hyduke D, Latif H, Portnoy V, Lewis N, Orth J . In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun. 2012; 3:929. PMC: 3827721. DOI: 10.1038/ncomms1928. View