bio_enzyme_rate: Fit a Michaelis-Menton Curve to Enzymatic Data

In BradyAJohnston/biochemr: Tools For User-Friendly & Reproducible Analysis of Biochemistry Experiments

Fit a Michaelis-Menton Curve to Enzymatic Data

Description

This function fits a Michaelis-Menton model to the dose-response data of enzymatic reactions. By default the minimum rate is locked to 0, if you wish to let the function find the minimum (if enzyme rate is > 0 at concentration 0) then supply the min = NA or manually set it i.e. min = 2.5.

Usage

bio_enzyme_rate(data, conc, rate, group = NULL, min = 0)

Arguments

data	Data frame with columns for concentration, reaction rate and optionally grouping information.
conc	Column containing the concentration data.
rate	Column containing the enzyme rate data.
group
min	Minimum value of enzyme rate. Defaults to 0, as if there is 0 substrate there should be no enzymatic activity in a properly blanked experiment. Set to NA to allow the model to fit the minimum value.

Value

tibble with nested list columns of data, model, predictions, residuals and coefficients.

Examples

# Fitting MM curves to the enzymatic data inside of datasets::Puromycin
library(bicohemr)
df <- Puromycin %>%
  bio_enzyme_rate(conc, rate, state)

# the result is a tibble with a column for the data, a column for the
# calculated # model and a column for the relevant coefficients extracted from
# the model
df

# you can extract the coefficient data using either `bio_coefs()` or
# `unnest()` on the column
bio_coefs(df)
tidyr::unnest(df, coefs)

# quick plot can be made for inspecing the results with `bio_plot()`
df %>%
  bio_plot()

BradyAJohnston/biochemr documentation built on April 13, 2022, 9:15 p.m.