Reversible Inhibition Explorer

Compare uninhibited and inhibited Michaelis-Menten kinetics across four diagnostic plots.

Configure the simulation

Pick the inhibition type, then set the enzyme's K_m, k_cat, and the inhibitor's [I], K_i, and K_i′. Use the Experimental Design tab for assay parameters ([E]₀, substrate range, replicates), and the Plots tab to visualize the result.

Inhibition type

Selecting a type pre-fills K_i and K_i′ below. Override either to construct edge cases — the type then auto-classifies from the values.

Presets

Enzyme parameters

K_m (μM)

k_cat (s⁻¹)

V_max = [E]₀·k_cat. (Set [E]₀ on the Experimental Design tab.)

Inhibitor parameters

[I] (μM)

K_i (μM)

K_i′ (μM)

K_i = inhibitor binding to free E. K_i′ = inhibitor binding to ES. The numeric input accepts very large values (e.g. 10⁹) to suppress one binding mode for the limiting cases — but the slider tops out at 500 μM for normal use.

Display

Data mode

Reveal answers

In experimental mode, each plot shows simulated noisy data and a least-squares fit (dashed line). Configure noise on the Plots tab. Hiding answers blanks the diagnostic readouts and reference lines — useful for problem sets.

Current configuration— α— α′—

Experimental design

Set the assay design — how much enzyme, what substrate range, how many replicates per concentration. These choices control how the noisy data look on the Plots tab.

Assay parameters

[E]₀ (μM)

[S]_max (× K_m)

# replicates 3

Substrate is sampled log-spaced from K_m/8 to [S]_max, with each [S] measured n times under independent Gaussian noise (set on the Plots tab). Replicates are shown as individual scatter points — the linear regression fits all of them.

The four diagnostic plots

Hyperbolic Michaelis-Menten plus three linearizations. Compare slopes, x-intercepts, and y-intercepts between the uninhibited (blue) and inhibited (red) series.

Measurement Noise

σ (% V_max) 2.0%

1 · Michaelis-Menten

v = V_max·[S] ⁄ (α·K_m + α′·[S])

2 · Lineweaver-Burk

1 ⁄ v = (K_m,app ⁄ V_max,app)·(1 ⁄ [S]) + 1 ⁄ V_max,app

3 · Hanes-Woolf

[S] ⁄ v = (1 ⁄ V_max,app)·[S] + K_m,app ⁄ V_max,app

4 · Eadie-Hofstee

v = −K_m,app·(v ⁄ [S]) + V_max,app

Diagnostics & classification

Each type of inhibition leaves a fingerprint: which constant changes, and by how much. On a Lineweaver-Burk plot, the slope ratio (inh/uninh) equals α and the y-intercept ratio equals α′.

Apparent constants (true values)

V_max

—

V_max,app

—

K_m

—

K_m,app

—

Lineweaver-Burk diagnostic ratios (inhibited / uninhibited)

Slope ratio (= α)

—

y-int ratio (= α′)

—

x-int ratio (= α′⁄α)

—

Auto-classified inhibition type

—

Quick reference — what each plot tells you:
Competitive: lines on LB share a y-intercept (V_max unchanged); K_m increases. Noncompetitive: lines share an x-intercept (K_m unchanged); V_max drops. Uncompetitive: lines are parallel (slope unchanged); both V_max and K_m drop proportionally. Mixed: slope, y-intercept, and x-intercept all change — K_i < K_i′ means tighter binding to free E than to ES; K_i > K_i′ means the reverse.

v = V_max[S] ⁄ (αK_m + α′[S]) · α = 1 + [I]⁄K_i · α′ = 1 + [I]⁄K_i′

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