enzyme_kinetics

install.packages(c("ggplot2", "dplyr", "purrr", "broom"))

#this code allows you to visualize the effects of different Km and Vmax values, which can be useful for questions regarding enzyme kinetics and the effects of different inhibitors. 

# Install and load necessary libraries
library(ggplot2)
library(dplyr)
library(purrr)
library(broom)

michaelis_menten <- function(S, Km, Vmax) {
  (Vmax * S) / (Km + S)
}

# Define substrate concentrations
S <- seq(from = 0.01, to = 100, by = 0.01)

# Define Km and Vmax values for three different enzymes
Km_values <- c(0.1, 0.2, 1)
Vmax_values <- c(10, 10, 10)

df <- data.frame()

for (i in 1:length(Km_values)){
  df <- rbind(df, data.frame(S = S,
                             V = michaelis_menten(S, Km_values[i], Vmax_values[i]),
                             Enzyme = paste("Enzyme", i)))
}

# Set the upper limit for substrate concentrations
S_upper_limit <- 10*max(Km_values)

# Filter out any data where S is greater than the upper limit
df <- df[df$S <= S_upper_limit,]

# Then plot
ggplot(df, aes(x = S, y = V, color = Enzyme)) +
  geom_line() +
  labs(x = "Substrate concentration (S)", y = "Reaction rate (V)", title = "Michaelis-Menten Plot for Three Enzymes") +
  theme_minimal() +
  geom_hline(yintercept = 0, color = "black", size = 1) +
  geom_vline(xintercept = 0, color = "black", size = 1)    


#lineweaver-burk
df <- data.frame()

for (i in 1:length(Km_values)){
  df <- rbind(df, data.frame(S = 1/S,  # take reciprocal of S
                             V = 1/michaelis_menten(S, Km_values[i], Vmax_values[i]),  # take reciprocal of V
                             Enzyme = paste("Enzyme", i)))
}

intercepts <- vector()
extended_df <- df %>% 
  split(.$Enzyme) %>%
  map_df(~{
    model <- lm(V ~ S, data = .)
    intercept <- -coef(model)[1] / coef(model)[2]
    intercepts <<- c(intercepts, intercept)
    data.frame(
      S = seq(min(intercepts), 15, length.out = 1000),
      V = predict(model, newdata = data.frame(S = seq(min(intercepts), 15, length.out = 1000))),
      Enzyme = unique(.$Enzyme)
    )
  }, .id = "Enzyme")

ggplot(extended_df, aes(x = S, y = V, color = Enzyme)) +
  geom_line() +
  labs(x = "1/Substrate concentration (1/S)", y = "1/Reaction rate (1/V)", title = "Extended Lineweaver-Burk Plot for Three Enzymes") +
  theme_minimal() +
  geom_hline(yintercept = 0, color = "black", size = 1) + 
  geom_vline(xintercept = 0, color = "black", size = 1)