bioRxiv2023
Kinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of bottom up parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard macroscopic kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and microscopic rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates. Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured using in vitro assays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.