In metabolic engineering or synthetic biology robust models with high predictive power are required. Constraints-based modelling methods such as metabolic flux analysis (MFA), flux balance analysis (FBA), elementary flux modes (EFMs) or extreme pathways (EP) have been widely used. The success of these methods is however conditioned by the many times insufficient mechanistic knowledge base. In this study, we built upon a previously developed hybrid constraints-based modelling method to develop E. coli models with improved predictive power. In particular, we apply a projection to latent pathways (PLP) method that merges together mechanistic and statistical constraints. It may be considered as a middle-out modelling approach that combines reliable knowledge and reverse engineering to extract unknown mechanisms from “omics” data sets. The method is applied to predict the central carbon fluxes of several E. coli strains (both wild-type and single gene KO mutants). We show that the central carbon fluxes of several single gene KO E. coli mutants could be predicted with high accuracy from the combined information of gene deletion and environmental conditions.