(A) Definition of sensitivity. As an example, the upper and bottom panels indicate the regression curve across the concentration gradient of glucose and the normalized regression curve, in which both the concentration gradient and the growth rates are rescaled within one unit, respectively. The shaded area was determined as the sensitivity (S) of glucose. (B) Variation in the sensitivity. Six different regression curves, i.e., six different S values, of glucose are shown, which result from the alternative combinations of the other 40 components (left panels). The yellow gradation and blue lines represent the variation in medium combinations and the corresponding regression curves, respectively (right panels). (C) Distributions of the mean sensitivities. The mean S values evaluated according to lag time (τ), growth rate (r), and saturated population size (K) are shown as Sτ, Sr, and SK, respectively. The sum of the three S values is shown as global sensitivity (Sg). The black lines indicate the fitting curves of the power law. (D) Most sensitive components. The components with the largest S values are shown in the order of value. (E) Balance of sensitivity. The balance of sensitivity is visualized by the triangle of Sr, SK, and Sτ in red dotted lines. The solid lines in pink, blue, and green represent Sr, SK, and Sτ, respectively. Those close to or far from an equilateral triangle are determined as the balanced (Ile) or biased (Cys) sensitivity in response to the growth phases, respectively. (F) Variance of sensitivity. The components with either the smallest or the largest Vs are shown in the order of value. Five components of either balanced or biased sensitivity are shown.