Integrating Kinetic Models of Metabolism with k-OptForce for Strain Design


Computational strain-design approaches relying solely on knowledge of model stoichiometry cannot capture the effect of enzyme activity levels and substrate-level enzyme regulation on metabolic flux redirection. In this talk, we apply the recently developed k-OptForce procedure which integrates the available kinetic descriptions of metabolic reactions with stoichiometric models, to sharpen the prediction of intervention strategies for improving the bio-production of chemicals of interest. The suggested interventions are comprised of both direct enzymatic activity changes (for reactions with available kinetics) and indirect reaction flux manipulations (for reactions with only stoichiometric information). In some cases, additional modifications are needed to overcome the substrate-level regulations imposed by the representative kinetic model, while in other cases, kinetic expressions shape flux distributions so as to favor the overproduction of the desired product requiring fewer direct interventions. k-OptForce requires as input kinetic expressions that accurately capture the substrate-level regulation of metabolic fluxes. To this end, we constructed a kinetic model of E. coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the recently introduced Ensemble Modeling (EM) concepts. This model consists of 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions and accounts for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions and a number of other reactions. Parameterization of the model was performed using a formal optimization algorithm that minimizes uncertainty-scaled discrepancies between model predictions and flux measurements. Application of k-OptForce for overproduction of bio-chemicals recapitulated existing intervention strategies, while identifying additional and alternate manipulations often distal to the point of regulations. This framework paves the way for an integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.