Enzyme Kinetic Analysis for the 21st Century

For much of the 20th century, enzyme kinetic analysis relied on deriving simplified rate equations under the steady-state approximation and later by analytical integration of differential equations for transient kinetics. This approach has since been surpassed by computational methods using numerical integration of rate equations to directly fit experimental data based on a complete user-defined model. This paradigm shift removes the constraints imposed by solving analytical equations, enabling far greater flexibility in experimental design and model complexity. Modern global fitting methods allow data from diverse experiments to be analyzed simultaneously using the minimum number of parameters supported by the information content of the data set. Global data fitting is more than just an algorithm for data analysis─it represents a fundamental change in how we design and interpret experiments, and eliminates many of the restrictions, approximations, and ambiguities inherent to equation-based analyses. In this review, we describe the principles and practice of global data fitting, compare the outcomes to conventional equation-based methods, and demonstrate its power through examples involving multiple experiments with distinct conditions and readouts. We explain why the common practice of making measurements in triplicate introduces uncertainty and we outline advanced methods for rigorously estimating errors in measurement and for establishing robust confidence limits on fitted parameters.