def get_flagella_initial_state(ports={}):
flagella_data = FlagellaChromosome()
chromosome_config = flagella_data.chromosome_config
molecules = {}
for nucleotide in nucleotides.values():
molecules[nucleotide] = 5000000
for amino_acid in amino_acids.values():
molecules[amino_acid] = 1000000
return {
ports.get('molecules', 'molecules'): molecules,
ports.get('transcripts', 'transcripts'):
{gene: 0
for gene in chromosome_config['genes'].keys()},
ports.get('proteins', 'proteins'): {
'CpxR': 10,
'CRP': 10,
'Fnr': 10,
'endoRNAse': 1,
'flagella': 8,
UNBOUND_RIBOSOME_KEY: 200, # e. coli has ~ 20000 ribosomes
UNBOUND_RNAP_KEY: 200
}
}
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 test_gene_expression(total_time=10):
# load the compartment
compartment_config = {
'external_path': ('external', ),
'global_path': ('global', ),
'agents_path': (
'..',
'..',
'cells',
),
'transcription': {
'sequence': toy_chromosome_config['sequence'],
'templates': toy_chromosome_config['promoters'],
'genes': toy_chromosome_config['genes'],
'promoter_affinities':
toy_chromosome_config['promoter_affinities'],
'transcription_factors': ['tfA', 'tfB'],
'elongation_rate': 10.0
},
# 'complexation': {
# 'monomer_ids': [],
# 'complex_ids': [],
# 'stoichiometry': {}}
}
compartment = GeneExpression(compartment_config)
molecules = {nt: 1000 for nt in nucleotides.values()}
molecules.update({aa: 1000 for aa in amino_acids.values()})
proteins = {
polymerase: 100
for polymerase in [UNBOUND_RNAP_KEY, UNBOUND_RIBOSOME_KEY]
}
proteins.update({factor: 1 for factor in ['tfA', 'tfB']})
# simulate
settings = {
'timestep': 1,
'total_time': total_time,
'initial_state': {
'proteins': proteins,
'molecules': molecules
}
}
return simulate_compartment_in_experiment(compartment, settings)
def __init__(self, initial_parameters=None):
if not initial_parameters:
initial_parameters = {}
super(RnaDegradation, self).__init__(initial_parameters)
self.derive_defaults('sequences', 'transcript_order', keys_list)
self.derive_defaults('catalysis_rates', 'protein_order', keys_list)
self.sequences = self.parameters['sequences']
self.catalysis_rates = self.parameters['catalysis_rates']
self.degradation_rates = self.parameters['degradation_rates']
self.transcript_order = self.parameters['transcript_order']
self.protein_order = self.parameters['protein_order']
self.molecule_order = list(nucleotides.values())
self.partial_transcripts = {
transcript: 0
for transcript in self.transcript_order
}
self.global_deriver_key = self.or_default(initial_parameters,
'global_deriver_key')
def plot_gene_expression_output(timeseries, config, out_dir='out'):
name = config.get('name', 'gene_expression')
ports = config.get('ports', {})
molecules = timeseries[ports['molecules']]
transcripts = timeseries[ports['transcripts']]
proteins = timeseries[ports['proteins']]
time = timeseries['time']
# make figure and plot
n_cols = 1
n_rows = 5
plt.figure(figsize=(n_cols * 6, n_rows * 1.5))
# define subplots
ax1 = plt.subplot(n_rows, n_cols, 1)
ax2 = plt.subplot(n_rows, n_cols, 2)
ax3 = plt.subplot(n_rows, n_cols, 3)
ax4 = plt.subplot(n_rows, n_cols, 4)
ax5 = plt.subplot(n_rows, n_cols, 5)
polymerase_ids = [UNBOUND_RNAP_KEY, UNBOUND_RIBOSOME_KEY]
amino_acid_ids = list(amino_acids.values())
nucleotide_ids = list(nucleotides.values())
# plot polymerases
for poly_id in polymerase_ids:
ax1.plot(time, proteins[poly_id], label=poly_id)
ax1.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
ax1.title.set_text('polymerases')
# plot nucleotides
for nuc_id in nucleotide_ids:
ax2.plot(time, molecules[nuc_id], label=nuc_id)
ax2.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
ax2.title.set_text('nucleotides')
# plot molecules
for aa_id in amino_acid_ids:
ax3.plot(time, molecules[aa_id], label=aa_id)
ax3.legend(loc='center left', bbox_to_anchor=(2.0, 0.5))
ax3.title.set_text('amino acids')
# plot transcripts
for transcript_id, series in transcripts.items():
ax4.plot(time, series, label=transcript_id)
ax4.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
ax4.title.set_text('transcripts')
# plot proteins
for protein_id in sorted(proteins.keys()):
if protein_id != UNBOUND_RIBOSOME_KEY:
ax5.plot(time, proteins[protein_id], label=protein_id)
ax5.legend(loc='center left', bbox_to_anchor=(1.5, 0.5))
ax5.title.set_text('proteins')
# adjust axes
for axis in [ax1, ax2, ax3, ax4, ax5]:
axis.spines['right'].set_visible(False)
axis.spines['top'].set_visible(False)
ax1.set_xticklabels([])
ax2.set_xticklabels([])
ax3.set_xticklabels([])
ax4.set_xticklabels([])
ax5.set_xlabel('time (s)', fontsize=12)
# save figure
fig_path = os.path.join(out_dir, name)
plt.subplots_adjust(wspace=0.3, hspace=0.5)
plt.savefig(fig_path, bbox_inches='tight')
from vivarium.core.composition import process_in_experiment
from vivarium.states.chromosome import Chromosome, Rnap, Promoter, frequencies, add_merge, toy_chromosome_config
from vivarium.library.polymerize import Elongation, build_stoichiometry, template_products
from vivarium.data.nucleotides import nucleotides
def choose_element(elements):
if elements:
choice = np.random.choice(len(elements), 1)
return list(elements)[int(choice)]
#: Variable name for unbound RNA polymerase
UNBOUND_RNAP_KEY = 'RNA Polymerase'
monomer_ids = list(nucleotides.values())
#: The default configuration parameters for :py:class:`Transcription`
class Transcription(Process):
name = 'transcription'
defaults = {
'promoter_affinities': {},
'transcription_factors': [],
'sequence': '',
'templates': {},
'genes': {},
'elongation_rate': 1.0,
'polymerase_occlusion': 5,
'symbol_to_monomer': nucleotides,