def test_transcription():
parameters = {
'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
}
chromosome = Chromosome(toy_chromosome_config)
transcription = Transcription(parameters)
experiment = process_in_experiment(
transcription, {
'initial_state': {
'chromosome': chromosome.to_dict(),
'molecules':
{nucleotide: 10
for nucleotide in transcription.monomer_ids},
'proteins': {
UNBOUND_RNAP_KEY: 10
},
'factors': {
'tfA': 0.2,
'tfB': 0.7
}
}
})
pp(experiment.state.get_value())
experiment.update(10.0)
pp(experiment.state.get_value())
print('complete!')
def generate_gfp_compartment(config):
media = Media()
PURE = {
key: value * units.mmol / units.L
for key, value in media.get_saved_media('PURE_Fuji_2014').items()
}
# TODO: deal with volume
volume = 1e-15 * units.L
mmol_to_counts = AVOGADRO.to('1/mmol') * volume.to('L')
print(mmol_to_counts)
PURE_counts = {
key: int(value * mmol_to_counts)
for key, value in PURE.items()
}
print(PURE)
print(PURE_counts)
plasmid = Chromosome(gfp_plasmid_config)
sequences = plasmid.product_sequences()
print(sequences)
proteins = {
UNBOUND_RNAP_KEY: PURE_counts[UNBOUND_RNAP_KEY],
UNBOUND_RIBOSOME_KEY: PURE_counts[UNBOUND_RIBOSOME_KEY],
'GFP': 0,
'endoRNAse': 1
}
gfp_config = {
'transcription': {
'sequence': gfp_plasmid_config['sequence'],
'templates': gfp_plasmid_config['promoters'],
'genes': gfp_plasmid_config['genes'],
'promoter_affinities': {
('T7', ): 0.001
},
'polymerase_occlusion': 30,
'elongation_rate': 50
},
'translation': {
'sequences': {
'GFP_RNA': GFP.sequence
},
'templates': {
'GFP_RNA': generate_template('GFP_RNA', len(GFP.sequence),
['GFP'])
},
'transcript_affinities': {
'GFP_RNA': 0.001
},
'elongation_rate': 22,
'polymerase_occlusion': 50
},
'degradation': {
'sequences': sequences,
'catalysis_rates': { # TODO: provide kcat for each RNA variety
'endoRNAse': 0
},
'degradation_rates': {
'transcripts': {
'endoRNAse': {
'GFP_RNA': 1e-23
}
}
}
},
'initial_state': {
'molecules': PURE_counts,
'transcripts': {
'GFP_RNA': 0
},
'proteins': proteins
}
}
return GeneExpression(gfp_config)
def __init__(self, initial_parameters=None):
'''A stochastic transcription model
.. WARNING:: Vivarium's knowledge base uses the gene name to
name the protein. This means that for a gene acrA that
codes for protein AcrA, you must refer to the gene,
transcript, and protein each as acrA.
:term:`Ports`:
* **chromosome**: The linked :term:`store` should hold a
representation of the chromosome in the form returned by
:py:meth:`vivarium.states.chromosome.Chromosome.to_dict`.
* **molecules**: Expects variables with the names in the
*molecule_ids* configuration. These are the monomers consumed
by transcription.
* **factors**: Expects variables for each transcription factor's
concentration.
* **transcripts**: The linked store should store the
concentrations of the transcripts.
* **proteins**: The linked store should hold the concentrations
of the transcription factors and the RNA polymerase.
Arguments:
initial_parameters: The following configuration options may
be provided:
* **promoter_affinities** (:py:class:`dict`): Maps from
binding state tuples to the binding affinity of RNA
polymerase and the promoter when the promoter is at
that binding state. The binding state of a promoter is
which (if any) transcription factors are bound to the
promoter. Such a binding state can be represented by a
binding state tuple, which is a :py:class:`tuple`
whose first element is the name of the promoter. All
bound transcription factors are listed as subsequent
elements. If no transcription factors are bound, the
sole subsequent element is ``None``.
.. todo:: What is the significance of the order in the
binding state tuple?
.. todo:: What are the units of the affinities?
* **transcription_factors** (:py:class:`list`): A list
of all modeled transcription factors.
* **sequence**: The DNA sequence that includes all the
genes whose transcription is being modeled.
* **templates** (:py:class:`dict`): Maps from the name
of an operon to that operon's :term:`template
specification`.
* **genes** (:py:class:`dict`): Maps from operon name to
a list of the names of the genes in that operon.
* **elongation_rate** (:py:class:`float`): The
elongation rate of the RNA polymerase.
* **polymerase_occlusion** (:py:class:`int`): The number
of base pairs behind the polymerase where another
polymerase is occluded and so cannot bind.
* **symbol_to_monomer** (:py:class:`dict`): Maps from
the symbols used to represent monomers in the RNA
sequence to the name of the free monomer. This should
generally be
:py:data:`vivarium.data.nucleotides.nucleotides`.
* **monomer_ids** (:py:class:`list`): A list of the
names of the free monomers consumed by transcription.
This can generally be computed as:
>>> from vivarium.data.nucleotides import nucleotides
>>> monomer_ids = nucleotides.values()
>>> print(list(monomer_ids))
['ATP', 'GTP', 'UTP', 'CTP']
Note that we only included the ``list()``
transformation to make the output prettier. The
``dict_values`` object returned by the ``.values()``
call is sufficiently list-like for use here.
* **molecule_ids** (:py:class:`list`): A list of all the
molecules needed by the :term:`process`. This will
generally be the same as *monomer_ids*.
Example configuring the process (uses
:py:func:`vivarium.library.pretty.format_dict`):
>>> import random
>>>
>>> import numpy as np
>>>
>>> from vivarium.states.chromosome import (
... toy_chromosome_config,
... Chromosome,
... )
>>> from vivarium.data.nucleotides import nucleotides
>>> # format_dict lets us print dictionaries prettily
>>> from vivarium.library.pretty import format_dict
>>>
>>> random.seed(0) # Needed because process is stochastic
>>> np.random.seed(0)
>>> # We will use the toy chromosome from toy_chromosome_config
>>> print(toy_chromosome_config)
{'sequence': 'ATACGGCACGTGACCGTCAACTTA', 'genes': {'oA': ['eA'], 'oAZ': ['eA', 'eZ'], 'oB': ['eB'], 'oBY': ['eB', 'eY']}, 'promoter_order': ['pA', 'pB'], 'promoters': {'pA': {'id': 'pA', 'position': 3, 'direction': 1, 'sites': [{'position': 0, 'length': 3, 'thresholds': {'tfA': <Quantity(0.3, 'millimolar')>}}], 'terminators': [{'position': 6, 'strength': 0.5, 'products': ['oA']}, {'position': 12, 'strength': 1.0, 'products': ['oAZ']}]}, 'pB': {'id': 'pB', 'position': -3, 'direction': -1, 'sites': [{'position': 0, 'length': 3, 'thresholds': {'tfB': <Quantity(0.5, 'millimolar')>}}], 'terminators': [{'position': -9, 'strength': 0.5, 'products': ['oB']}, {'position': -12, 'strength': 1.0, 'products': ['oBY']}]}}, 'promoter_affinities': {('pA', None): 1.0, ('pA', 'tfA'): 10.0, ('pB', None): 1.0, ('pB', 'tfB'): 10.0}, 'domains': {0: {'id': 0, 'lead': 0, 'lag': 0, 'children': []}}, 'rnaps': {}}
>>> monomer_ids = list(nucleotides.values())
>>> configuration = {
... 'promoter_affinities': {
... ('pA', None): 1.0,
... ('pA', 'tfA'): 10.0,
... ('pB', None): 1.0,
... ('pB', 'tfB'): 10.0},
... 'transcription_factors': ['tfA', 'tfB'],
... 'sequence': toy_chromosome_config['sequence'],
... 'templates': toy_chromosome_config['promoters'],
... 'genes': toy_chromosome_config['genes'],
... 'elongation_rate': 10.0,
... 'polymerase_occlusion': 5,
... 'symbol_to_monomer': nucleotides,
... 'monomer_ids': monomer_ids,
... 'molecule_ids': monomer_ids,
... }
>>> # At this point we haven't used the toy chromosome yet
>>> # because it will be specified in the chromosome port.
>>> # Notice that the parameters are specific to the chromosome.
>>> transcription_process = Transcription(configuration)
>>> # Now we need to initialize the simulation stores
>>> state = {
... 'chromosome': toy_chromosome_config,
... 'molecules': {
... nucleotide: 10
... for nucleotide in monomer_ids
... },
... 'proteins': {UNBOUND_RNAP_KEY: 10},
... 'factors': {'tfA': 0.2 * units.mM, 'tfB': 0.7 * units.mM},
... }
>>> update = transcription_process.next_update(1.0, state)
>>> print(update['chromosome'])
{'rnaps': {'_add': [{'path': (2,), 'state': <class 'vivarium.states.chromosome.Rnap'>: {'id': 2, 'template': 'pA', 'template_index': 0, 'terminator': 1, 'domain': 0, 'state': 'polymerizing', 'position': 7}}, {'path': (3,), 'state': <class 'vivarium.states.chromosome.Rnap'>: {'id': 3, 'template': 'pB', 'template_index': 1, 'terminator': 0, 'domain': 0, 'state': 'occluding', 'position': 3}}, {'path': (4,), 'state': <class 'vivarium.states.chromosome.Rnap'>: {'id': 4, 'template': 'pA', 'template_index': 0, 'terminator': 0, 'domain': 0, 'state': 'occluding', 'position': 0}}], '_delete': []}, 'rnap_id': 4, 'domains': {0: <class 'vivarium.states.chromosome.Domain'>: {'id': 0, 'lead': 0, 'lag': 0, 'children': []}}, 'root_domain': 0}
'''
if not initial_parameters:
initial_parameters = {}
log.debug(
'inital transcription parameters: {}'.format(initial_parameters))
super(Transcription, self).__init__(initial_parameters)
self.derive_defaults('templates', 'promoter_order', keys_list)
self.derive_defaults('templates', 'transcript_ids', template_products)
self.sequence = self.parameters['sequence']
self.templates = self.parameters['templates']
self.genes = self.parameters['genes']
empty_chromosome = Chromosome({
'sequence': self.sequence,
'promoters': self.templates,
'genes': self.genes
})
self.sequences = empty_chromosome.sequences()
self.symbol_to_monomer = self.parameters['symbol_to_monomer']
log.debug('chromosome sequence: {}'.format(self.sequence))
self.promoter_affinities = self.parameters['promoter_affinities']
self.promoter_order = self.parameters['promoter_order']
self.promoter_count = len(self.promoter_order)
self.transcription_factors = self.parameters['transcription_factors']
self.molecule_ids = self.parameters['molecule_ids']
self.molecule_ids.extend(['ATP', 'ADP'])
self.monomer_ids = self.parameters['monomer_ids']
self.transcript_ids = self.parameters['transcript_ids']
self.elongation = 0
self.elongation_rate = self.parameters['elongation_rate']
self.polymerase_occlusion = self.parameters['polymerase_occlusion']
self.stoichiometry = build_stoichiometry(self.promoter_count)
self.initiation = StochasticSystem(self.stoichiometry,
random_seed=np.random.randint(
2**31))
self.protein_ids = [UNBOUND_RNAP_KEY] + self.transcription_factors
self.initial_domains = self.parameters['initial_domains']
self.concentrations_deriver_key = self.parameters[
'concentrations_deriver_key']
self.chromosome_ports = ['rnaps', 'rnap_id', 'domains', 'root_domain']
log.debug('final transcription parameters: {}'.format(self.parameters))
def next_update(self, timestep, states):
chromosome_state = states['chromosome']
# chromosome_state['rnaps'] = list(chromosome_state['rnaps'].values())
original_rnap_keys = [
rnap['id'] for rnap in chromosome_state['rnaps'].values()
]
chromosome = Chromosome(self.chromosome_config(chromosome_state))
molecules = states['molecules']
proteins = states['proteins']
factors = states['factors'] # as concentrations
promoter_rnaps = chromosome.promoter_rnaps()
promoter_domains = chromosome.promoter_domains()
# Find out how many promoters are currently blocked by a
# newly initiated or occluding rnap
promoter_count = len(chromosome.promoter_order)
blocked_promoters = np.zeros(promoter_count, dtype=np.int64)
open_domains = {}
bound_domains = {}
for promoter_index, promoter_key in enumerate(
chromosome.promoter_order):
domains = []
for rnap in promoter_rnaps.get(promoter_key, {}).values():
if rnap.is_occluding():
domains.append(rnap.domain)
blocked_promoters[promoter_index] += 1
bound_domains[promoter_key] = set(domains)
open_domains[promoter_key] = promoter_domains[
promoter_key] - bound_domains[promoter_key]
blocked_promoters = np.array(blocked_promoters)
# Make the state for a gillespie simulation out of total number of each
# promoter by copy number not blocked by initiated rnap,
# concatenated with the number of each promoter that is bound by rnap.
# These are the two states for each promoter the simulation
# will operate on, essentially going back and forth between
# bound and unbound states.
copy_numbers = chromosome.promoter_copy_numbers()
original_unbound_rnaps = proteins[UNBOUND_RNAP_KEY]
monomer_limits = {
monomer: molecules[monomer]
for monomer in self.monomer_ids
}
unbound_rnaps = original_unbound_rnaps
time = 0
now = 0
elongation = Elongation(self.sequences, chromosome.promoters,
monomer_limits, self.symbol_to_monomer,
self.elongation)
initiation_affinity = self.build_affinity_vector(
chromosome.promoters, factors)
while time < timestep:
# build the state vector for the gillespie simulation
substrate = np.concatenate([
copy_numbers - blocked_promoters, blocked_promoters,
[unbound_rnaps]
])
log.debug('transcription substrate: {}'.format(substrate))
log.debug('blocked promoters: {}'.format(blocked_promoters))
# find number of monomers until next terminator
distance = 1 / self.elongation_rate # chromosome.terminator_distance()
# find interval of time that elongates to the point of the next terminator
interval = min(distance, timestep - time)
if interval == distance:
# perform the elongation until the next event
terminations, monomer_limits, chromosome.rnaps = elongation.step(
interval, monomer_limits, chromosome.rnaps)
unbound_rnaps += terminations
else:
elongation.store_partial(interval)
terminations = 0
log.debug('time: {} --- interval: {}'.format(time, interval))
log.debug('monomer limits: {}'.format(monomer_limits))
log.debug('terminations: {}'.format(terminations))
# run simulation for interval of time to next terminator
result = self.initiation.evolve(interval, substrate,
initiation_affinity)
log.debug('result: {}'.format(result))
# perform binding
for now, event in zip(result['time'], result['events']):
# RNAP has bound the promoter
promoter_key = chromosome.promoter_order[event]
promoter = chromosome.promoters[promoter_key]
domains = open_domains[promoter_key]
domain = choose_element(domains)
blocked_promoters[event] += 1
bound_domains[promoter_key].add(domain)
open_domains[promoter_key].remove(domain)
# create a new bound RNAP and add it to the chromosome.
new_rnap = chromosome.bind_rnap(event, domain)
new_rnap.start_polymerizing()
log.debug('newly bound RNAP: {}'.format(new_rnap))
unbound_rnaps -= 1
# deal with occluding rnap
for rnap in chromosome.rnaps.values():
if rnap.is_unoccluding(self.polymerase_occlusion):
log.debug('RNAP unoccluding: {}'.format(rnap))
blocked_promoters[rnap.template_index] -= 1
bound_domains[rnap.template].remove(rnap.domain)
open_domains[rnap.template].add(rnap.domain)
rnap.unocclude()
log.debug('rnap: {}'.format(rnap))
log.debug('complete: {}'.format(elongation.complete_polymers))
time += interval
# track how far elongation proceeded to start from next iteration
self.elongation = elongation.elongation - int(elongation.elongation)
proteins = {UNBOUND_RNAP_KEY: unbound_rnaps - original_unbound_rnaps}
molecules = {
key: count * -1
for key, count in elongation.monomers.items()
}
# 1 ATP hydrolysis cost per nucleotide elongation
molecules['ATP'] = 0
molecules['ADP'] = 0
for count in elongation.monomers.values():
molecules['ATP'] -= count
molecules['ADP'] += count
chromosome_dict = chromosome.to_dict()
rnaps = chromosome_dict['rnaps']
original = set(original_rnap_keys)
current = set(rnaps.keys())
bound_rnaps = current - original
completed_rnaps = original - current
continuing_rnaps = original - completed_rnaps
rnap_updates = {
rnap_id: rnaps[rnap_id]
for rnap_id in continuing_rnaps
}
add_rnaps = [{
'path': (bound, ),
'state': rnaps[bound]
} for bound in bound_rnaps]
delete_rnaps = [(completed, ) for completed in completed_rnaps]
rnap_updates['_add'] = add_rnaps
rnap_updates['_delete'] = delete_rnaps
chromosome_dict['rnaps'] = rnap_updates
update = {
'chromosome':
{key: chromosome_dict[key]
for key in self.chromosome_ports},
'proteins': proteins,
'molecules': molecules,
'transcripts': elongation.complete_polymers
}
log.debug('molecules update: {}'.format(update['molecules']))
return update
def __init__(self, parameters={}):
if self.knowledge_base is None:
self.knowledge_base = KnowledgeBase()
if self.ecoli_sequence is None:
self.ecoli_sequence = read_sequence(ECOLI_GENOME_PATH)
self.factor_thresholds = {
('flhDp', 'CRP'): 1e-05 * units.mM,
('fliLp1', 'flhDC'): 1e-06 * units.mM,
('fliLp1', 'fliA'): 1.3e-05 * units.mM,
('fliEp1', 'flhDC'): 4e-06 * units.mM,
('fliEp1', 'fliA'): 1.1e-05 * units.mM,
('fliFp1', 'flhDC'): 7e-06 * units.mM,
('fliFp1', 'fliA'): 1e-05 * units.mM,
('flgBp', 'flhDC'): 1e-05 * units.mM,
('flgBp', 'fliA'): 8e-06 * units.mM,
('flgAp', 'flhDC'): 1.3e-05 * units.mM,
('flgAp', 'fliA'): 6e-06 * units.mM,
('flhBp', 'flhDC'): 1.5e-05 * units.mM,
('flhBp', 'fliA'): 5e-06 * units.mM,
('fliAp1', 'flhDC'): 1.7e-05 * units.mM,
('fliAp1', 'fliA'): 4e-06 * units.mM,
('flgEp', 'flhDC'): 1.9e-05 * units.mM,
('flgEp', 'fliA'): 3e-06 * units.mM,
('fliDp', 'flhDC'): 1.9e-05 * units.mM,
('fliDp', 'fliA'): 3e-06 * units.mM,
('flgKp', 'flhDC'): 2.1e-05 * units.mM,
('flgKp', 'fliA'): 1e-06 * units.mM,
('fliCp', 'fliA'): 5e-06 * units.mM,
('tarp', 'fliA'): 7e-06 * units.mM,
('motAp', 'fliA'): 9e-06 * units.mM,
('flgMp', 'fliA'): 1.1e-06 * units.mM
}
self.factor_thresholds.update(parameters.get('thresholds', {}))
self.chromosome_config = {
'sequence': self.ecoli_sequence,
'genes': {
'flhDC': ['flhD', 'flhC'],
'fliL':
['fliL', 'fliM', 'fliN', 'fliO', 'fliP', 'fliQ', 'fliR'],
'fliE': ['fliE'],
'fliF': ['fliF', 'fliG', 'fliH', 'fliI', 'fliJ', 'fliK'],
'flgA': ['flgA', 'flgM', 'flgN'],
'flgM': ['flgM', 'flgN'],
'flgE': ['flgE'],
'flgB': [
'flgB', 'flgC', 'flgD', 'flgE', 'flgF', 'flgG', 'flgH',
'flgI', 'flgJ'
],
'flhB': ['flhB', 'flhA', 'flhE'],
'fliA': ['fliA', 'fliZ'], # ignore 'tcyJ' for now
'fliD': ['fliD', 'fliS', 'fliT'],
'flgK': ['flgK', 'flgL'],
'fliC': ['fliC'],
'tar': ['tar', 'tap', 'cheR', 'cheB', 'cheY', 'cheZ'],
'motA': ['motA', 'motB', 'cheA', 'cheW']
},
'promoters': {
'flhDp': {
'id':
'flhDp',
'position':
1978197,
'direction':
-1,
'sites': [{
'thresholds': {
'CRP': 1e-05
}
}],
'terminators': [{
'position': 1977266,
'strength': 1.0,
'products': ['flhDC']
}]
},
'fliLp1': {
'id':
'fliLp1',
'position':
2019618,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 4e-06
}
}, {
'thresholds': {
'fliA': 1.3e-05
}
}],
'terminators': [{
'position': 2023678,
'strength': 1.0,
'products': ['fliL']
}]
},
'fliEp1': {
'id':
'fliEp1',
'position':
2013014,
'direction':
-1,
'sites': [{
'thresholds': {
'flhDC': 5e-06
}
}, {
'thresholds': {
'fliA': 1.1e-05
}
}],
'terminators': [{
'position': 2012700,
'strength': 1.0,
'products': ['fliE']
}]
},
'fliFp1': {
'id':
'fliFp1',
'position':
2013229,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 7e-06
}
}, {
'thresholds': {
'fliA': 1e-05
}
}],
'terminators': [{
'position': 2019513,
'strength': 1.0,
'products': ['fliF']
}]
},
'flgBp': {
'id':
'flgBp',
'position':
1130863,
'direction':
-1,
'sites': [{
'thresholds': {
'flhDC': 1e-05
}
}, {
'thresholds': {
'fliA': 8e-06
}
}],
'terminators': [{
'position': 1129414,
'strength': 1.0,
'products': ['flgA']
}]
},
'flgAp': {
'id':
'flgAp',
'position':
1131018,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 1.3e-05
}
}, {
'thresholds': {
'fliA': 6e-06
}
}],
'terminators': [{
'position': 1138312,
'strength': 1.0,
'products': ['flgB']
}]
},
'flhBp': {
'id':
'flhBp',
'position':
1966191,
'direction':
-1,
'sites': [{
'thresholds': {
'flhDC': 1.5e-05
}
}, {
'thresholds': {
'fliA': 5e-06
}
}],
'terminators': [{
'position': 1962580,
'strength': 1.0,
'products': ['flhB']
}]
},
'fliAp1': {
'id':
'fliAp1',
'position':
2001789,
'direction':
-1,
'sites': [{
'thresholds': {
'flhDC': 1.7e-05
}
}, {
'thresholds': {
'fliA': 4e-06
}
}],
'terminators': [{
'position': 1999585,
'strength': 1.0,
'products': ['fliA']
}]
},
'flgEp': {
'id':
'flgEp',
'position':
1132574,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 1.9e-05
}
}, {
'thresholds': {
'fliA': 3e-06
}
}],
'terminators': [{
'position': 1133782,
'strength': 1.0,
'products': ['flgE']
}]
},
'fliDp': {
'id':
'fliDp',
'position':
2003872,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 1.9e-05
}
}, {
'thresholds': {
'fliA': 3e-06
}
}],
'terminators': [{
'position': 2006078,
'strength': 1.0,
'products': ['fliD']
}]
},
'flgKp': {
'id':
'flgKp',
'position':
1138378,
'direction':
1,
'sites': [{
'thresholds': {
'flhDC': 2.1e-05
}
}, {
'thresholds': {
'fliA': 1e-06
}
}],
'terminators': [{
'position': 1140986,
'strength': 1.0,
'products': ['flgK']
}]
},
'fliCp': {
'id':
'fliCp',
'position':
2002110,
'direction':
1,
'sites': [{
'thresholds': {
'fliA': 5e-06
}
}],
# {'thresholds': {'GadE': 0.55, 'H-NS': 0.6}}],
'terminators': [{
'position': 2003606,
'strength': 1.0,
'products': ['fliC']
}]
},
'tarp': {
'id':
'tarp',
'position':
1972691,
'direction':
-1,
'sites': [{
'thresholds': {
'fliA': 7e-06
}
}],
# {'thresholds': {'Fnr': 1e-5}}
'terminators': [{
'position': 1971030,
'strength': 1.0,
'products': ['tar']
}]
},
'motAp': {
'id':
'motAp',
'position':
1977139,
'direction':
-1,
'sites': [{
'thresholds': {
'fliA': 9e-06
}
}],
# {'thresholds': {'CpxR': 1e-5}}],
'terminators': [{
'position': 1976252,
'strength': 1.0,
'products': ['motA']
}]
},
'flgMp': {
'id':
'flgMp',
'position':
1130128,
'direction':
-1,
'sites': [{
'thresholds': {
'fliA': 1.1e-05
}
}],
# {'thresholds': {'CsgD': 0.1}}
'terminators': [{
'position': 1129414,
'strength': 1.0,
'products': ['flgM']
}]
}
},
'domains': {
0: {
'id': 0,
'lead': 0,
'lag': 0,
'children': []
}
},
'rnaps': {}
}
# build chromosome and apply thresholds
self.chromosome = Chromosome(self.chromosome_config)
self.chromosome.apply_thresholds(self.factor_thresholds)
self.chromosome_config['promoters'] = {
key: promoter.to_dict()
for key, promoter in self.chromosome.promoters.items()
}
self.promoters = [
'flhDp', 'fliLp1', 'fliEp1', 'fliFp1', 'flgAp', 'flgBp', 'flhBp',
'fliAp1', 'fliDp', 'flgKp', 'fliCp', 'tarp', 'motAp'
'flgMp'
]
self.flhDC_activated = [
'fliLp1', 'fliEp1', 'fliFp1', 'flgAp', 'flgBp', 'flgEp', 'flhBp',
'fliAp1', 'fliDp', 'flgKp'
]
self.fliA_activated = ['fliCp', 'tarp', 'motAp', 'flgMp']
flhDC_factors = {
'fliLp1': {
'flhDC': 1.2,
'fliA': 0.25
},
'fliEp1': {
'flhDC': 0.45,
'fliA': 0.35
},
'fliFp1': {
'flhDC': 0.35,
'fliA': 0.30
},
'flgBp': {
'flhDC': 0.35,
'fliA': 0.45
},
'flgAp': {
'flhDC': 0.15,
'fliA': 0.3
},
'flgEp': {
'flhDC': 1.0,
'fliA': 4.0
},
'flhBp': {
'flhDC': 0.1,
'fliA': 0.35
},
'fliAp1': {
'flhDC': 1.0,
'fliA': 0.3
},
'fliDp': {
'flhDC': 1.2,
'fliA': 0.25
},
'flgKp': {
'flhDC': 1.2,
'fliA': 0.25
}
}
def binary_sum_gates(promoter_factors):
affinities = {}
first, second = list(promoter_factors[list(
promoter_factors.keys())[0]].keys())
# this hard coding of simple addition is alarming and probably points
# towards providing a function of promoter state for affinity rather
# than a simple lookup of the affinity for each promoter state tuple.
for promoter, factors in promoter_factors.items():
affinities[(promoter, first, None)] = factors[first]
affinities[(promoter, None, second)] = factors[second]
affinities[(promoter, first,
second)] = factors[first] + factors[second]
return affinities
self.promoter_affinities = {('flhDp', 'CRP'): 0.01}
# self.promoter_affinities[('motAp', 'CpxR')] = 1.0
flhDC_affinities = binary_sum_gates(flhDC_factors)
self.promoter_affinities.update(flhDC_affinities)
# for promoter in self.flhDC_activated:
# self.promoter_affinities[(promoter, 'flhDC')] = 1.0
for promoter in self.fliA_activated:
self.promoter_affinities[(promoter, 'fliA')] = 1.0
self.promoter_affinities.update(
parameters.get('promoter_affinities', {}))
self.transcripts = [
(operon, product)
for operon, products in self.chromosome_config['genes'].items()
for product in products
]
self.protein_sequences = {
(operon, product): self.knowledge_base.proteins[
self.knowledge_base.genes[product]['id']]['seq']
for operon, product in self.transcripts
}
self.transcript_templates = {
key: generate_template(key, len(sequence), [key[1]])
for key, sequence in self.protein_sequences.items()
}
# transcript affinities are the affinities transcripts to bind a ribosome and translate to protein
# transcript affinities are scaled relative to the requirements to build a single full flagellum.
self.min_tr_affinity = parameters.get('min_tr_affinity', 1e-1)
tr_affinity_scaling = {
'fliL': 2,
'fliM': 34,
'fliG': 26,
'fliH': 12,
'fliI': 6,
'fliD': 5,
'flgE': 120
}
self.transcript_affinities = {}
for (operon, product) in self.transcripts:
self.transcript_affinities[(
operon,
product)] = self.min_tr_affinity * tr_affinity_scaling.get(
product, 1)
self.transcript_affinities.update(
parameters.get('transcript_affinities', {}))
self.transcription_factors = [
'flhDC', 'fliA', 'CsgD', 'CRP', 'GadE', 'H-NS', 'CpxR', 'Fnr'
]
self.complexation_monomer_ids = [
'fliG', 'fliM', 'fliN', 'flhA', 'flhB', 'flhD', 'flhC', 'fliO',
'fliP', 'fliQ', 'fliR', 'fliJ', 'fliI', 'fliH', 'fliL', 'flgH',
'motA', 'motB', 'flgB', 'flgC', 'flgF', 'flgG', 'flgI', 'fliF',
'fliE', 'fliC', 'flgL', 'flgK', 'fliD', 'flgE'
]
self.complexation_complex_ids = [
'flhDC', 'flagellar motor switch', 'flagella',
'flagellar export apparatus', 'flagellar motor'
]
self.complexation_stoichiometry = {
'flhDC': {
'flhD': -4.0,
'flhC': -2.0,
'flhDC': 1.0
},
'flagellar motor switch reaction': {
'flagellar motor switch': 1.0,
'fliG': -26.0,
'fliM': -34.0,
'fliN': -1.0
},
'flagellar export apparatus reaction': {
'flagellar export apparatus': 1.0,
'flhA': -1.0,
'flhB': -1.0,
'fliO': -1.0,
'fliP': -1.0,
'fliQ': -1.0,
'fliR': -1.0,
'fliJ': -1.0,
'fliI': -6.0,
'fliH': -12.0
},
'flagellar motor reaction': {
'flagellar motor': 1.0,
'flagellar motor switch': -1.0,
'fliL': -2.0,
'flgH': -1.0,
'motA': -1.0,
'motB': -1.0,
'flgB': -1.0,
'flgC': -1.0,
'flgF': -1.0,
'flgG': -1.0,
'flgI': -1.0,
'fliF': -1.0,
'fliE': -1.0
},
'flagellum reaction': {
'flagella': 1.0,
'flagellar export apparatus': -1.0,
'flagellar motor': -1.0,
'fliC': -1.0,
'flgL': -1.0,
'flgK': -1.0,
'fliD': -5.0,
'flgE': -120.0
}
}
reaction_default = 1e-5
self.complexation_rates = {
'flhDC': reaction_default,
'flagellar motor switch reaction': reaction_default,
'flagellar export apparatus reaction': reaction_default,
'flagellar motor reaction': reaction_default,
'flagellum reaction': reaction_default
}