fE.progress

Fits the progress curve of an enzyme-catalyzed reaction.

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

fE.progress function

<dl><dt>data</dt>
<dd>a dataframe where the first column is the time and the second column is the substrate concentration.</dd>
<dt>unit_S</dt>
<dd>concentration unit.</dd>
<dt>unit_t</dt>
<dd>time unit.</dd></dl>

Arguments

Fitted Progress Curve for Enzyme-Catalyzed Reaction — fE.progress

<dl>

<dt>data</dt>
<dd>a dataframe where the first column is the time and the second column is the substrate concentration.</dd>


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


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

</dl>

fE.progress: Fitted Progress Curve for Enzyme-Catalyzed Reaction

Description

Usage

Value

Arguments

References

See Also

Examples