Figure 2.

BVA can discriminate between static and dynamic heterogeneity. (A) E^∗ histogram of three simulated, equimolar static species with $\langle E_1^{\ast }\rangle =0.4$, $\langle E_2^{\ast }\rangle =0.5$, and $\langle E_3^{\ast }\rangle =0.6$. PDA predictions (upper) either assuming the presence of the three known static components (black line) or fitting for a two-state dynamic model (red line;$\langle E_1^{\ast }\rangle =0.373\pm 0.003$, $\langle E_2^{\ast }\rangle =0.627\pm 0.003$, $k_{1\to 2}=k_{2\to 1}=883\pm 17{\text{s}}^{-1}$) could adequately account for the observed E^∗ distribution (${\chi }_r^2<2$). BVA of the same sample, however (lower), correctly shows no evidence for dynamics, i.e., all $s_{E^{\ast }}$ fall below the confidence interval (black triangles). (B) E^∗ histogram of a simulated dynamic species fluctuating with the fitted two-state dynamic parameters from A. PDA predictions (upper) are shown for the three-species static model (black line; $\langle E_1^{\ast }\rangle =0.382\pm 0.002$, $\langle E_2^{\ast }\rangle =0.5$ (fixed), $\langle E_3^{\ast }\rangle =0.611\pm 0.001$), and the two-state dynamic model (red line). As in A, either source of heterogeneity could explain the observed E^∗ distribution (${\chi }_r^2<2$). However, BVA (lower) showed clear evidence for dynamics (red triangles; $s_{E^{\ast }}$ of intermediate E^∗ fall above the confidence interval).