def run_master(out_dir):
timeseries = test_master()
volume_ts = timeseries['global']['volume']
print('growth: {}'.format(volume_ts[-1] / volume_ts[0]))
expression_plot_settings = {
'name': 'gene_expression',
'ports': {
'transcripts': 'transcripts',
'molecules': 'metabolites',
'proteins': 'proteins'
}
}
plot_gene_expression_output(timeseries, expression_plot_settings, out_dir)
plot_settings = {
'max_rows':
20,
'remove_zeros':
True,
'skip_ports': [
'prior_state', 'null', 'flux_bounds', 'chromosome', 'reactions',
'exchange'
]
}
plot_simulation_output(timeseries, plot_settings, out_dir)
def run_flagella_compartment(
compartment,
initial_state=None,
out_dir='out'):
# get flagella data
flagella_data = FlagellaChromosome()
# run simulation
settings = {
# a cell cycle of 2520 sec is expected to express 8 flagella.
# 2 flagella expected in approximately 630 seconds.
'total_time': 2520,
'emit_step': COMPARTMENT_TIMESTEP,
'verbose': True,
'initial_state': initial_state}
timeseries = simulate_compartment_in_experiment(compartment, settings)
# save reference timeseries
save_timeseries(timeseries, out_dir)
plot_config = {
'name': 'flagella_expression',
'ports': {
'transcripts': 'transcripts',
'proteins': 'proteins',
'molecules': 'molecules'}}
plot_gene_expression_output(
timeseries,
plot_config,
out_dir)
# just-in-time figure
plot_config2 = plot_config.copy()
plot_config2.update({
'name': 'flagella',
'plot_ports': {
'transcripts': list(flagella_data.chromosome_config['genes'].keys()),
'proteins': flagella_data.complexation_monomer_ids + flagella_data.complexation_complex_ids,
'molecules': list(nucleotides.values()) + list(amino_acids.values())}})
plot_timeseries_heatmaps(
timeseries,
plot_config2,
out_dir)
# make a basic sim output
plot_settings = {
'max_rows': 30,
'remove_zeros': True,
'skip_ports': ['chromosome', 'ribosomes']}
plot_simulation_output(
timeseries,
plot_settings,
out_dir)
def run_chemotaxis_master(out_dir):
total_time = 10
# make the compartment
compartment = ChemotaxisMaster({})
# save the topology network
settings = {'show_ports': True}
plot_compartment_topology(compartment, settings, out_dir)
# run an experinet
settings = {'timestep': 1, 'total_time': total_time}
timeseries = simulate_compartment_in_experiment(compartment, settings)
volume_ts = timeseries['boundary']['volume']
print('growth: {}'.format(volume_ts[-1] / volume_ts[0]))
# plots
# simulation output
plot_settings = {
'max_rows': 40,
'remove_zeros': True,
'skip_ports': ['reactions', 'prior_state', 'null']
}
plot_simulation_output(timeseries, plot_settings, out_dir)
# gene expression plot
gene_exp_plot_config = {
'name': 'flagella_expression',
'ports': {
'transcripts': 'transcripts',
'proteins': 'proteins',
'molecules': 'internal'
}
}
plot_gene_expression_output(timeseries, gene_exp_plot_config, out_dir)
}
}
return GeneExpression(gfp_config)
if __name__ == '__main__':
out_dir = os.path.join('out', 'tests', 'gfp_expression_composite')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# load the compartment
gfp_expression_compartment = generate_gfp_compartment()
# run simulation
settings = {
'total_time': 100,
}
timeseries = simulate_compartment(gfp_expression_compartment, settings)
plot_config = {
'name': 'gfp_expression',
'ports': {
'transcripts': 'transcripts',
'molecules': 'molecules',
'proteins': 'proteins'
}
}
plot_gene_expression_output(timeseries, plot_config, out_dir)