Data for Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response

Data from simulations used to generate the figures in the paper <em>Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response</em>. Extracted folder contains the following items: <em>sim_dfs</em>: a folder containing the CSV files that must be moved into the <em>data </em>folder of the <em>vivarium-ecoli</em> repository before using <em>ecoli/analysis/antibiotics_colony/plot.py</em> to generate any figures <em>glc_10000_fluxome.csv</em>: Each row represents a reaction in central carbon metabolism (in same order as listed in <em>validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv</em>). 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 <em>ecoli/analysis/centralCarbonMetabolism.py </em>script to reproduce fluxome validation plot. <em>glc_10000_proteome_avgs.csv</em>: Each row represents a protein monomer (in same order as <em>sim_data.translation.monomer_data["id"]</em> where <em>sim_data</em> is <em>reconstruction/sim_data/kb/validationData.cPickle</em>). 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 <em>ecoli/analysis/proteinCountsValidation.py</em> script to reproduce proteome validation plot. <em>glc_10000_expressome.csv</em>: 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 <em>data/subgen_gene_plots/generate_plots.py</em> script to calculate number of sub-generational/exponential genes among all genes and antibiotic response genes.