(639e) Parameterization of Large-Scale Kinetic Models of Metabolism Using Datasets with Different Reference States

Large-scale kinetic models of metabolism provide the computational means to dynamically link reaction fluxes in a cell to metabolite concentrations and enzyme levels while also accounting for substrate level regulation. However, the development of broadly applicable frameworks to efficiently and robustly parameterize these models remains a challenge. Challenges arising from the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data causes degeneracy of obtained parameter solutions and complicate the interpretability of results. Additionally, computational demands for solving the parameter identification problem limits widespread adoption of large-scale kinetic models, despite their potential. To address challenges in both data and computation, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models of Escherichia coli, Saccharomyces cerevisiae, and Clostridium thermocellum. and is not only at least an order of magnitude faster than the gradient-based algorithm K-FIT but also able to obtain better data fits. This tool presents an opportunity for construction of parameterized kinetic models with improved predictive capabilities and thereby enabling more complex descriptors of metabolic capacities as well as improved strain design tools.