On Over-Parameterisation and Parameter Estimation of Enzyme Kinetics

The estimation of kinetic parameters based on experiments is an important element in understanding the reaction mechanisms of enzymes and their intrinsic properties. However, increasing model complexity by introducing multiple parameters can lead to overparameterisation, resulting in poor parameter identifiability and potentially causing the model to describe noise rather than underlying biochemical mechanisms. In this study, we use the total quasi-steady-state assumption to clarify whether the parameters of multi-parameter models can be correctly identified even with a high number of parameters. Therefore, a basic model was used, and the number of parameters was increased successively. A Bayesian optimisation approach was applied, which predicted the next experiments with the highest information density in order to reduce the experimental effort required for the experiments. The results show, on the one hand, that the parameters of multi-parameter models can indeed be correctly identified. On the other hand, it also shows that under certain conditions, incorrect values were estimated, even though the consideration of confidence intervals suggested correct identification.