Fit a Michaelis-Menton Curve to Enzymatic Data

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.

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)
#> Error in library(bicohemr): there is no package called ‘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
#> # A tibble: 2 × 4
#>   state     data              model  coefs           
#>   <fct>     <list>            <list> <list>          
#> 1 treated   <tibble [12 × 2]> <drc>  <tibble [2 × 5]>
#> 2 untreated <tibble [11 × 2]> <drc>  <tibble [2 × 5]>

# you can extract the coefficient data using either `bio_coefs()` or
# `unnest()` on the column
bio_coefs(df)
#> # A tibble: 4 × 6
#>   state     term  estimate std.error statistic  p.value
#>   <fct>     <chr>    <dbl>     <dbl>     <dbl>    <dbl>
#> 1 treated   Vmax  213.       7.16        29.7  4.37e-11
#> 2 treated   Km      0.0641   0.00871      7.36 2.42e- 5
#> 3 untreated Vmax  160.       6.81        23.5  2.14e- 9
#> 4 untreated Km      0.0477   0.00842      5.67 3.07e- 4
tidyr::unnest(df, coefs)
#> # A tibble: 4 × 8
#>   state     data              model  term  estimate std.error statistic  p.value
#>   <fct>     <list>            <list> <chr>    <dbl>     <dbl>     <dbl>    <dbl>
#> 1 treated   <tibble [12 × 2]> <drc>  Vmax  213.       7.16        29.7  4.37e-11
#> 2 treated   <tibble [12 × 2]> <drc>  Km      0.0641   0.00871      7.36 2.42e- 5
#> 3 untreated <tibble [11 × 2]> <drc>  Vmax  160.       6.81        23.5  2.14e- 9
#> 4 untreated <tibble [11 × 2]> <drc>  Km      0.0477   0.00842      5.67 3.07e- 4

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