ausblenden:
Schlagwörter:
enzyme parameter; Michaelis-Menten constant; regression model; statistical learning
Zusammenfassung:
Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical
systems. For most kinetic parameters, however, not even an order of magnitude is known, so the
estimation of model parameters from experimental data remains a major task in systems biology.
We propose a statistical approach to infer values for kinetic parameters across species and enzymes
making use of parameter values that have been measured under various conditions and that are
nowadays stored in databases. We fit the data by a statistical regression model in which the
substrate, the combination enzyme-substrate and the combination organism-substrate have a linear
effect on the logarithmic parameter value. As a result, we obtain predictions and error ranges for
unknown enzyme parameters. We apply our method to decadic logarithmic Michaelis-Menten
constants from the BRENDA database and confirm the results with leave-one-out crossvalidation,
in which we mask one value at a time and predict it from the remaining data. For a set of 8
metabolites we obtain a standard prediction error of 1.01 for the deviation of the predicted values
from the true values, while the standard deviation of the experimental values is 1.16. The method
is applicable to other types of kinetic parameters for which many experimental data are available.
