Genome scale mapping models and algorithms for stationary and instationary MFA based metabolic flux elucidation

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism, neglecting degradation pathways and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up core bacterial and cyanobacterial mapping models to a genome-scale carbon mapping (GSCM) models imEco726 (668 reaction and 566 metabolites) and imSyn711 (731 reactions, 679 metabolites) for E. coli and Synechocystis PCC 6803, respectively. In both models, 80% of all fluxes varies less than one-tenth of the basis carbon substrate uptake rate primarily due to the flux coupling with biomass production. While topology and estimated values of the metabolic fluxes remain largely consistent between the core and GSCM models for E. coli, significant flux range shifts are seen in Synechocystis arising from differences in utilization of the pentose phosphate pathway, photorespiration, and anaplerotic metabolism. In comparison, wider flux inference ranges for 20 key central metabolic reactions were seen using a GSCM model of E. coli due to the existence of alternate routes and accurate accounting of biomass precursor requirements. Flux ranges obtained with GSCM models are compared with those obtained upon projecting core model ranges on to a genome-scale metabolic model to elucidate the loss of information and erroneous biological inferences about pathway usage arising from assumptions contained within core models, reaffirming the importance of using mapping models with global carbon path coverage in 13C metabolic flux analysis.