iCHO2441 genome-scale metabolic model
Creation of updated CHO Genome-scale metabolic model, iCHO2441 iCHO1766 was obtained from the Bigg models database (King et al., 2016), while iCHO2291 was obtained via the BioModels database (Malik-Sheriff et al., 2020). The expanded iCHO2441 GeM was constructed by coupling the secretory machinery presented within the iCHO2048 (Gutierrez et al., 2020a) to the recently published updated iCHO2291 (Yeo et al., 2020a). This was achieved by adapting the Jupyter Notebooks developed by Gutierrez et al. (2020) to use the updated iCHO2291 as a base model to which secretory reactions may be added. In brief, information of each secreted product including amino acid composition, presence of a signal peptide, number of disulphide bonds, number of core N-linked glycans, and molecular weight were fed to the notebook and used to add the appropriate secretory pathway reactions to the model (Table S1). For intracellular flux prediction, a custom model was generated for each secreted product using product composition data. For auxotrophy and gene essentiality predictions, a generic IgG structure was used to add secretory reactions to the model (Table S1). As per the recommendation by Hart et al., genes with transformed values less than -3 were classed as ‘not expressed/ negative confidence’ (class -1). Genes with transformed values between -3 and -1.5 were classed as ‘low confidence expressed’ (class 1). Genes with transformed values between -1.5 and 0 were classed as ‘medium confidence expressed’ (class 2). Genes with transformed values greater than 0 were classed as ‘expressed/high confidence’ (class 3). These classifications were mapped to reactions within iCHO2441 using the method described above, where unclassified reactions were given a class of 0. Reconstructions were generated per experiment using the CORDA function for python which took iCHO2441 constrained with experimental uptakes (Table S2) as a base model and the gene confidence classifications as inputs. An in-depth rationale behind this novel approach is presented in supplementary material 1.