Figure 2.

The estimation of a single parameter (k _cat or K _M) with either the sQ or the tQ model. For each condition (S _T = 0.2, 2, or 80 nM, and E _T = 0.2, 2, or 40 nM), 10⁵ posterior samples of either k _cat (a) or K _M (b) were obtained by applying the Bayesian inference to 10² noisy data sets (Fig. S1) (see Methods for details). When the k _cat is sampled, the K _M is fixed at its true value (a) and vice versa (b). Here, green triangles indicate the true values of the parameters. Whereas the estimates of k _cat and K _M obtained with the sQ model are biased as E _T increases, those obtained with the tQ model have negligible bias regardless of conditions (See Fig. S3 for box plots of estimates). As E _T or S _T increases, the posterior variance of K _M increases when the tQ model is used.