Chapter 9 Regulation in Avida

9.1 Overview

total_updates <- 200000
replicates <- 100

all_traits <- c("not","nand","and","ornot","or","andnot")
traits_set_a <- c("not", "and", "or")
traits_set_b <- c("nand", "ornot", "andnot")

# Relative location of data.
working_directory <- "experiments/2021-02-08-evo-dynamics/analysis/" # << For bookdown
# working_directory <- "./"                                              # << For local analysis

9.2 Analysis dependencies

Load all required R libraries.

library(ggplot2)
library(tidyverse)
library(cowplot)
library(RColorBrewer)
library(Hmisc)
library(boot)
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")

These analyses were conducted/knitted with the following computing environment:

print(version)

##                _                           
## platform       x86_64-pc-linux-gnu         
## arch           x86_64                      
## os             linux-gnu                   
## system         x86_64, linux-gnu           
## status                                     
## major          4                           
## minor          1.3                         
## year           2022                        
## month          03                          
## day            10                          
## svn rev        81868                       
## language       R                           
## version.string R version 4.1.3 (2022-03-10)
## nickname       One Push-Up

9.3 Setup

trace_summary_data_loc <- paste0(working_directory, "data/trace_summary.csv")
trace_summary_data <- read.csv(trace_summary_data_loc, na.strings="NONE")

trace_summary_data$DISABLE_REACTION_SENSORS <- as.factor(trace_summary_data$DISABLE_REACTION_SENSORS)
trace_summary_data$chg_env <- trace_summary_data$chg_env == "True"
trace_summary_data$sensors <- trace_summary_data$DISABLE_REACTION_SENSORS == "0"


env_label_fun <- function(chg_env) {
  if (chg_env) {
    return("Fluctuating")
  } else {
    return("Constant")
  }
}

sensors_label_fun <- function(has_sensors) {
  if (has_sensors) {
    return("Sensors")
  } else {
    return("No sensors")
  }
}

# note that this labeler makes assumptions about how we set up our experiment
condition_label_fun <- function(has_sensors, env_chg) {
  if (has_sensors && env_chg) {
    return("PLASTIC")
  } else if (env_chg) {
    return("NON-PLASTIC")
  } else {
    return("STATIC")
  }
}

trace_summary_data$env_label <- mapply(
  env_label_fun,
  trace_summary_data$chg_env
)
trace_summary_data$sensors_label <- mapply(
  sensors_label_fun,
  trace_summary_data$sensors
)
trace_summary_data$condition <- mapply(
  condition_label_fun,
  trace_summary_data$sensors,
  trace_summary_data$chg_env
)

####### misc #######
# Configure our default graphing theme
theme_set(theme_cowplot())
dir.create(paste0(working_directory, "plots"), showWarnings=FALSE)

9.4 How many instructions do plastic genomes toggle depending on environmental context?

ggplot(trace_summary_data, aes(x=dominant_num_toggled_sites)) +
    geom_histogram(
      binwidth=1,
      color="black"
    ) +
    scale_fill_brewer(
      palette="Paired"
    ) +
    scale_color_brewer(
      palette="Paired"
    ) +
    scale_x_continuous(
      breaks=seq(0, max(trace_summary_data$dominant_num_toggled_sites)+1)
    ) +
    theme(
      legend.position="none"
    )

ggsave(paste0(working_directory, "plots/", "toggled-sites.png"))

## Saving 7 x 5 in image

9.5 What is the distrubution of toggled sequence sizes?

chunk_sizes <- data.frame(
  size=integer()
)
for (sizes in trace_summary_data$dominant_toggled_chunk_sizes) {
  if (sizes == "") { next }
  sizes <- unlist(lapply(str_split(sizes, ';'), as.integer))
  chunk_sizes <- rbind(chunk_sizes, data.frame(size=c(sizes)))
}

ggplot(chunk_sizes, aes(x=size)) +
    geom_histogram(
      binwidth=1,
      color="black"
    ) +
    scale_fill_brewer(
      palette="Paired"
    ) +
    scale_color_brewer(
      palette="Paired"
    ) +
    scale_x_continuous(
      name="toggled sequence size",
      breaks=seq(0, 10),
      limits=c(0, 10)
    ) +
    theme(
      legend.position="none"
    )

ggsave(paste0(working_directory, "plots/", "toggled-chunk-sizes.png"))

## Saving 7 x 5 in image