Data from simulations used to generate the figures in the paper Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response. Extracted folder contains the following items: sim_dfs: a folder containing the CSV files that must be moved into the data folder of the vivarium-ecoli repository before using ecoli/analysis/antibiotics_colony/plot.py to generate any figures glc_10000_fluxome.csv: Each row represents a reaction in central carbon metabolism (in same order as listed in validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux given in units of mmol/L/hr. Provided as input to ecoli/analysis/centralCarbonMetabolism.py script to reproduce fluxome validation plot. glc_10000_proteome_avgs.csv: Each row represents a protein monomer (in same order as sim_data.translation.monomer_data["id"] where sim_data is reconstruction/sim_data/kb/validationData.cPickle). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to ecoli/analysis/proteinCountsValidation.py script to reproduce proteome validation plot. glc_10000_expressome.csv: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to data/subgen_gene_plots/generate_plots.py script to calculate number of sub-generational/exponential genes among all genes and antibiotic response genes.