Lineweaver-Burk Calculator Formula
Understand the math behind the lineweaver-burk calculator. Each variable explained with a worked example.
Formulas Used
Y-Intercept (1/Vmax)
y_intercept = 1 / vmaxSlope (Km/Vmax)
slope = km / vmaxX-Intercept (-1/Km)
x_intercept = -1 / kmVariables
| Variable | Description | Default |
|---|---|---|
vmax | Vmax(umol/min) | 100 |
km | Km(uM) | 50 |
How It Works
Lineweaver-Burk Plot
The Lineweaver-Burk (double reciprocal) plot linearizes the Michaelis-Menten equation by plotting 1/v vs 1/[S].
Formula
1/v = (Km/Vmax) × (1/[S]) + 1/Vmax
This is a straight line with slope = Km/Vmax, y-intercept = 1/Vmax, and x-intercept = -1/Km. While historically important, nonlinear regression is preferred today because Lineweaver-Burk overweights low-concentration data.
Worked Example
An enzyme with Vmax = 100 umol/min and Km = 50 uM.
- 01Y-intercept = 1/100 = 0.01
- 02Slope = 50/100 = 0.5
- 03X-intercept = -1/50 = -0.02
Frequently Asked Questions
Why is Lineweaver-Burk still taught?
It provides a visual way to distinguish inhibition types: competitive inhibition changes the slope but not the intercept, while noncompetitive changes the intercept but not the slope. These patterns are easily seen on the LB plot.
What are the disadvantages?
The double reciprocal transformation distorts experimental error, giving excessive weight to the least precise measurements (at low [S]). Nonlinear least-squares fitting of the untransformed data is statistically superior.
What other linearization methods exist?
Eadie-Hofstee (v vs v/[S]), Hanes-Woolf ([S]/v vs [S]), and direct linear plots. Each has different error-weighting properties. Eadie-Hofstee is considered the best linear method.
Ready to run the numbers?
Open Lineweaver-Burk Calculator