Fig 2. Sensitivity analysis and parameter correlations.

(a) Using model 6 (see Table 1), for a given value $x_i=p_i/p_i^{*}$ of the parameter in the legend, the mean error χ² is minimized with respect to all other parameters. The resulting minimum ${\tilde{\chi }}^2\left(x_i\right)$ versus x_i for all individual parameters shown in the legend. (b) Same as (a) except $k_{\text{off}}^{\text{P1}}$ is included as an additional fitting parameter. The dependence of error function on $k_{\text{off}}^{\text{P1}}$ is weak as long as $k_{\text{off}}^{\text{P1}}$ is large enough. This underconstrained problem, which causes the irregularities visible in some curves, is resolved by fixing $k_{\text{off}}^{\text{P1}}=100s^{-1}$ in model 6. (c) When we minimize χ² with the parameter $k_f^P$ fixed at the value shown in the horizontal axis, the other parameters assume values that depend on $k_f^P$ as shown here. The absolute value of the slope of the dependence, S_ji, between the fitted parameter j and the fixed parameter i characterizes the correlation between the two parameters. (d) S_ji (see (c)) for all pairs of parameters in model 6 are shown in the correlation matrix with the fixed parameters in the columns and the fitted parameters in the rows. The slopes in (c) are represented as the last row in (d).