Cell Biology: The Foundation of Life Sciences

# Install (if needed) and load ggplot2
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2", repos = "https://cloud.r-project.org")
}
library(ggplot2)

# R: Prokaryotic Growth Analysis
# Modeling bacterial population dynamics

# Exponential growth model
exponential_growth <- function(N0, r, t) {
    N0 * exp(r * t)
}

# Logistic growth model
logistic_growth <- function(N0, r, K, t) {
    (K * N0 * exp(r * t)) / (K + N0 * (exp(r * t) - 1))
}

# Growth parameters for different bacteria
bacteria_data <- data.frame(
    species = c("E. coli", "B. subtilis", "S. aureus", "M. tuberculosis"),
    doubling_time_min = c(20, 30, 28, 720),  # minutes
    growth_rate = log(2) / c(20, 30, 28, 720) * 60,  # per hour
    optimal_temp = c(37, 37, 37, 37),
    environment = c("Intestinal", "Soil", "Pathogenic", "Pathogenic")
)

# Simulation parameters
time_hours <- seq(0, 24, 0.5)
N0 <- 100  # Initial population
K <- 1e9   # Carrying capacity

# Generate growth curves for first 3 species (M. tuberculosis is too slow)
growth_curves <- data.frame()
for(i in 1:3) {
    species <- bacteria_data$species[i]
    r <- bacteria_data$growth_rate[i]
    
    exp_pop <- exponential_growth(N0, r, time_hours)
    log_pop <- logistic_growth(N0, r, K, time_hours)
    
    temp_data <- data.frame(
        time = rep(time_hours, 2),
        population = c(exp_pop, log_pop),
        model = rep(c("Exponential", "Logistic"), each = length(time_hours)),
        species = species
    )
    
    growth_curves <- rbind(growth_curves, temp_data)
}

# Create growth curves plot
growth_plot <- ggplot(growth_curves, aes(x = time, y = population, 
                                         color = species, linetype = model)) +
    geom_line(size = 1) +
    scale_y_log10(labels = scales::trans_format("log10", scales::math_format(10^.x))) +
    labs(title = "Bacterial Growth Patterns",
         subtitle = "Exponential vs Logistic Growth Models",
         x = "Time (hours)",
         y = "Population Size (log scale)",
         color = "Species",
         linetype = "Growth Model") +
    theme_minimal() +
    theme(legend.position = "bottom") +
    geom_hline(yintercept = K, linetype = "dashed", alpha = 0.5) +
    annotate("text", x = 20, y = K*1.5, label = "Carrying Capacity", size = 3)

print(growth_plot)

# Create doubling time comparison
doubling_plot <- ggplot(bacteria_data, aes(x = reorder(species, doubling_time_min), 
                                           y = doubling_time_min, 
                                           fill = environment)) +
    geom_col(alpha = 0.8) +
    scale_y_log10() +
    scale_fill_viridis_d() +
    labs(title = "Bacterial Doubling Times",
         x = "Species",
         y = "Doubling Time (minutes, log scale)",
         fill = "Environment") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    geom_text(aes(label = paste(doubling_time_min, "min")), 
              vjust = -0.2, size = 3)

print(doubling_plot)

cat("\n=== Prokaryotic Growth Analysis ===\n")
cat("Doubling times:\n")
for(i in 1:nrow(bacteria_data)) {
    cat(sprintf("  %s: %.0f minutes (%.2f hours)\n", 
                bacteria_data$species[i],
                bacteria_data$doubling_time_min[i],
                bacteria_data$doubling_time_min[i]/60))
}

# Calculate populations after 12 hours
cat("\nPopulation after 12 hours (exponential growth):\n")
for(i in 1:3) {
    final_pop <- exponential_growth(N0, bacteria_data$growth_rate[i], 12)
    cat(sprintf("  %s: %s cells\n", 
                bacteria_data$species[i],
                formatC(final_pop, format = "e", digits = 2)))
}

# Growth rate comparison
fastest <- bacteria_data[which.min(bacteria_data$doubling_time_min), ]
slowest <- bacteria_data[which.max(bacteria_data$doubling_time_min), ]
cat("\nGrowth rate extremes:\n")
cat("  Fastest:", fastest$species, "(", fastest$doubling_time_min, "min)\n")
cat("  Slowest:", slowest$species, "(", slowest$doubling_time_min, "min)\n")
cat("  Ratio:", round(slowest$doubling_time_min / fastest$doubling_time_min), "×\n")