Abstract
<jats:p>Linearized methods remain widely used in enzyme kinetics despite longstanding concerns regarding parameter estimation accuracy. We systematically investigate how experimental design influences parameter estimation in linearized enzyme kinetics models. Using the Lineweaver–Burk, Hanes–Woolf, and Eadie–Hofstee transformations, we show that the accuracy of Vmax and Km is not an intrinsic property of the linearization method, but emerges from the interaction between experimental design, substrate distribution, noise, and the position of substrate concentrations relative to Km. Systematic simulations across a wide range of experimental conditions reveal that each method redistributes experimental error differently between slope and intercept, resulting in distinct sensitivities to specific experimental regimes. While no single method is universally optimal, Eadie–Hofstee consistently provides the most robust performance across conditions, whereas Lineweaver–Burk and Hanes–Woolf show strong dependence on sampling strategy and substrate range. These findings demonstrate that selecting a linearization method should be guided by experimental design rather than convention, as inappropriate combinations of method and data structure can introduce substantial systematic bias even under controlled noise conditions.</jats:p>