dir.MM

Non-linear least-squares fitting of the Michaelis-Menten equation.

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

dir.MM function

<dl><dt>data</dt>
<dd>a dataframe with two columns. The first column contains the values of the independent variable (substrate concentration), and the second column contains the initial rates.</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 curve are plotted.</dd></dl>

Arguments

Non-linear Least-squares Fitting of the MM equation — dir.MM

<dl>

<dt>data</dt>
<dd>a dataframe with two columns. The first column contains the values of the independent variable (substrate concentration), and the second column contains the initial rates.</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 curve are plotted.</dd>

</dl>

dir.MM: Non-linear Least-squares Fitting of the MM equation

Description

Usage

Value

Arguments

Details

Examples