(626h) Kinetic Optforce: Integrating Kinetics With FBA for Strain Design

Authors: 
Chowdhury, A. - Presenter, The Pennsylvania State University
Zomorrodi, A. R., The Pennsylvania State Univeristy
Maranas, C. D., The Pennsylvania State University



In this talk we will introduce Kinetic OptForce, building upon our previous stoichiometric-based strain design protocol, OptForce, by integrating available kinetic models of metabolism with constraint-based models to sharpen intervention strategies in the enzyme and reaction levels for improving the production of the chemical of interest. Incorporation of the kinetic information contracts the feasible space for reaction fluxes, thereby leading to more streamlined identification of reactions that must be altered from their reference state in the face of overproduction target (i.e., MUST sets). The MUST sets are subsequently used to identify a minimal set of interventions, comprising of enzymatic changes (for reactions with available kinetic information) and flux alternations (for reactions lacking such information), forcing the network to meet a pre-specified overproduction target (i.e., FORCE set). This procedure was used first to study the overproduction of L-serine in E. coli. Kinetic OptForce revealed interventions necessary to remove the substrate-level inhibition of key enzymes in the central carbon metabolism to enhance the flux flow towards L-serine, which regular OptForce relying solely on stoichiometry cannot identify. In addition, Kinetic OptForce points at the cause of infeasibility (usually concentration bound violation) of intervention strategies found by regular OptForce. For the second case study, we explored the overproduction of triacetic acid lactone (TAL) in S. cerevisiae. Here a higher yield of TAL was predicted with fewer interventions using Kinetic OptForce. In this example kinetics are consistent with the flux redirections needed for overproduction. Preliminary experience with the use of kinetics in strain design reveals that both the number and nature of interventions are altered. Interventions tend to cause less dramatic rearrangements of metabolism so as not to violate concentration bounds. Importantly, the impact of interventions on concentration pools is quantified and additional model improvements can be gleaned by contrasting with experimental results.