Obtains Km and Vm using the Hanes-Woolf transformation.

Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization
methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the
ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation
of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential
biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten
equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making
use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate
the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.

Juan Aledo

renz

R-Enzymology

hw function

<dl><dt>data</dt>
<dd>a dataframe where the first column is the independent variable, [S], and the remaining columns (as many as experiment replicates) correspond to the dependent variable, v.</dd>
<dt>unit_S</dt>
<dd>concentration unit.</dd>
<dt>unit_v</dt>
<dd>time unit.</dd>
<dt>plot</dt>
<dd>logical. If TRUE the data and fitted line are plotted.</dd></dl>

Arguments

Hanes-Woolf Transformation — hw

<dl>

<dt>data</dt>
<dd>a dataframe where the first column is the independent variable, [S], and the remaining columns (as many as experiment replicates) correspond to the dependent variable, v.</dd>


<dt>unit_S</dt>
<dd>concentration unit.</dd>


<dt>unit_v</dt>
<dd>time unit.</dd>


<dt>plot</dt>
<dd>logical. If TRUE the data and fitted line are plotted.</dd>

</dl>

hw: Hanes-Woolf Transformation

Description

Usage

Value

Arguments

See Also

Examples