In silico co-factor balance estimation using constraint-based modelling informs metabolic engineering in Escherichia coli
Fig 6
MOMA compared to MFA-derived estimates, carbon yield efficiencies and CBA co-factor profile comparison across unconstrained, manually curated and experimentally constrained solutions.
(A) Flux ranges calculated with MOMA (green) and Metabolic Flux Analysis (orange stripes). MOMA ranges were estimated using the wild type solution as a reference and sequentially implementing the single-gene knockouts studied by Long et al. (2019) [46], with biomass formation as the objective function. MFA ranges were extracted from a pre-existing dataset (Long et al., 2019), using a Python algorithm to select the minimal and maximal flux ranges.(B) Carbon yields of butanol and butanol precursor models, compared across all approaches evaluated in this study: unconstrained pFBA (labelled ‘FBA’); manually curated pFBA solutions with minimized high-flux futile cycling (labelled ‘cFBA’); experimentally-constrained solutions using MFA-derived flux data (labelled ‘mFBA’); experimentally-constrained solutions using MFA-derived flux data with further capping in co-factor cycling reactions (labelled ‘cmCBA’) (C) ATP (blue) and NAD(P)H (yellow) CBA-derived cofactor usage profiles compared across all approaches evaluated in this study (labels identical as previously).