chapter 3 - Bentham Science

Enzyme Kinetics for Novice Learners Applications of Spreadsheets in Education The Amazing Power of a Simple Tool 109
difference equations or see [7]) are given on this tab of the Excelet if you scroll down below the
graph, a sample of which is shown in Fig. (6.2).
Fig. (6.2): Concentration over time plot.
A similar plot is employed on the Initial Rate S tab that shows how the initial rate, which is a
common measurement in enzyme experiments, is determined from the graph.
6.3 Transformed Data
If the enzyme kinetics data follows the form of the Michaelis-Menten equation then mathematically
it is a rectangular hyperbola and can be fit by non-linear regression techniques or the data can be
transformed to a linear plot and fit using linear regression. We look at the common transformations
(Table 6.1) used by biochemists [9] to fit the data to find the various parameters: vmax, KM, and kcat
(see transformed data tab). Historically, the data were transformed to produce linear plots, which in
the pre-technology age was the only way to analyze the data (using rulers and graph paper). Analysis
improved with the addition of linear regression, especially employing computational technology.
A big advantage of linear plots is the ability to spot behavior that is non-linear. Many errors that
occur when using the transformed data are pointed out in [10].
The three plots corresponding to the transformations in Table 6.1 are shown in Fig. (6.3) with
the lowest concentration value as a green point. One might notice how the points are not regularly
spaced, especially on the Lineweaver-Burk and Eadie-Hofstee plots. We explore these three
methods simultaneously and their sensitivity to random and systematic error, both constant and
proportional. If students need an introduction to investigating error, see [11, 12].