Fig. 5: Predictive capabilities of flux balance analysis using the genome-scale combined thermodynamic and stoichiometric model of E. coli constrained by g^diss_lim.

a, Predictions of physiological rates for E. coli growth on glucose with growth maximization as objective and the g^diss_lim of −4.9 kJ gCDW⁻¹ h⁻¹ as a constraint (solid black line). Red circles represent experimentally determined values from glucose-limited chemostat cultures^{24,58,59,60,61}, and red triangles values from glucose batch cultures⁶². The black arrow points to the GUR, at which the maximum growth rate was obtained in the simulation; solid grey lines represent predictions above this GUR and shaded grey area the variability determined through variability analysis. b, Predictions of maximal growth phenotype for growth on eight different carbon sources, on simultaneously present glucose and succinate, or on either glucose or glycerol supplemented with all proteinogenic amino acids, in all cases allowing for unlimited carbon source uptake^63,64. Horizontal error bars represent variability determined at optimal solution. The goodness of FBA predictions was assessed using the Pearson correlation coefficient (r), where the P values were derived using Student’s t-test. c, Concentration profiles of three metabolites (coenzyme A, CoA; ribose-5-phosphate, r5p; and α-ketoglutarate, akg), which in our simulations were correlated with GUR, and for which experimental data were available. The experimental metabolite profiles were obtained in accelerostat experiments with the E. coli strain MG1655 (ref. ⁵⁹). Here the onset of acetate production occurred at a lower GUR of 3.6 mmol gCDW⁻¹ h⁻¹. For both predictions and experimental data, the concentration profiles (solid grey line) were obtained using a local polynomial regression method.