maudr is an R package designed to generate, distribute, and evaluate enzyme kinetics data for undergraduate teaching. A typical use scenario of the package is for instructors to produce individual datasets for students that contain absorbance values for an inhibited and non-inhibited reaction of alcohol dehydrogenase over a range of substrate concentrations and with different inhibitors. Because individual datasets analysed by students would then require individual solutions to be calculated and visualised by instructors, the package also produces the plots and estimated Km and Vmax values from individual students' datasets and comparisons of inhibited and non-inhibited conditions.
More information about the package is available on the maudr website and in the vignette.
maudr is only available on GitHub, there are no plans to upload it to CRAN.
install.packages("remotes")
remotes::install_github("jarekbryk/maudr")The package uses enzyme kinetic parameters for S. cerevisiae alcohol dehydrogenase, as established in a 1987 publication by Ganzhorn et al.. An Excel file with the parameters for ADH and its inhibitors is provided by default (reaction_parameters.xlsx), however, parameters for other enzymes and inhibitors can be provided by the user, as long as the column names remain unchanged (Fig. 1.):
If the default parameters are used, the only input file required for the package is an Excel file with a list of students for whom the datasets will be generated and analysed. This file must have one row per student, with the following column headers (Fig. 2.):
The package has four main functions to produce students' datasets and analysis of their results; the functions should be used in order. They are meant to be used in order and are not pipeable on purpose, to enable instructors to inspect the output after each step.
Sets up a top-level folder (default location: current folder) where the input data will be stored and output produced. The top-level folder will be named "maudr_assignments" unless a different name is specified by the user. Folders data and output are created, with subfolders output/assignments_output and output/answers_output. RStudio project structure is not used.
Takes a list of students and a table with enzyme parameters and creates a metadata table with an assignment of every student-to-reaction conditions, randomly drawn from the reaction_parameters.xlsx file. This table is deposited in the output/assignments_output folder in the top-level project folder created by initialiseProject().
If neither of the input files are provided, this function runs in a demo mode, using a provided list of four imaginary students and enzyme parameters for the ADH to proceed.
The output of assignReactions() is a list with a timestamp and the metadata table with student-reaction assignments. The timestamp is used as an input for the next function and serves as a unique identifier for each run of the package.
Takes the timestamp from assignReactions() as input (and, invisibly, the student-reaction assignments table) and produces Excel file for each student with absorbances for the reactions with their assigned substrate and inhibitor. The files are deposited in the output/assignments_output folder with a given timestamp.
Takes the timestamp as input (and, invisibly, all the student-specific files) and produces PDF file with the results of the analysis of students' data. The PDF files can be generated individually for each student as separate files, or all together in a single file, or both. The PDF files are deposited in the output/assignments_output folder with a given timestamp.
library(maudr)
initialiseProject(path = "~/Desktop")
setup <- assignReactions(student_file = "~/Documents/students_file.xlsx") # students_file.xlsx is copied into the data folder in the top level folder; the default reaction_parameters.xlsx is used.
generateAssignments(run_timestamp = setup$timestamp, use_jitter = FALSE) # Assignments (one Excel file per student) will be deposited in the output/assignment_output folder
generateAnswers(run_timestamp = setup$timestamp, output_files = "both") # Assignments (one PDF file per student plus a single PDf with answers with all students' datasets) will be deposited in the output/answers_output folderUse of the nls function to plot and estimate Michaelis-Menten curves was inspired by this 2015 post by prof. Paul Brennan describing prof. Rob Benton's script on how to use it. We are also grateful to Fonti Kar for helpful suggestions that improved the package.