def test_memory():
stoichiometric_matrix, rates, initial_state, final_state = load_complexation(
)
duration = 1
amplify = 100
this = psutil.Process(os.getpid())
memory = memory_previous = this.memory_info().rss
memory_increases = 0
print('initial memory use: {}'.format(memory))
system = StochasticSystem(stoichiometric_matrix,
random_seed=np.random.randint(2**31))
obsidian_start = seconds_since_epoch()
for i in range(1, amplify + 1):
memory = this.memory_info().rss
if memory > memory_previous:
print('memory use before iteration {:2d}: {}'.format(i, memory))
memory_previous = memory
memory_increases += 1
result = system.evolve(duration, initial_state, rates)
difference = np.abs(final_state - result['outcome']).sum()
if difference:
print('difference is {}'.format(difference))
obsidian_end = seconds_since_epoch()
print('obsidian C implementation elapsed seconds for {} runs: {}'.format(
amplify, obsidian_end - obsidian_start))
assert memory_increases <= 1
def test_complexation():
fixtures_root = os.path.join('data', 'complexation')
def load_state(filename):
with open(os.path.join(fixtures_root, filename)) as f:
state = np.array(json.load(f))
return state
initial_state = load_state('initial_state.json')
final_state = load_state('final_state.json')
assert initial_state.size == final_state.size
n_metabolites = initial_state.size
with open(os.path.join(fixtures_root, 'stoichiometry.json')) as f:
stoichiometry_sparse = json.load(f)
n_reactions = len(stoichiometry_sparse)
stoichiometric_matrix = np.zeros((n_metabolites, n_reactions), np.int64)
for (reaction_index, reaction_stoich) in enumerate(stoichiometry_sparse):
for (str_metabolite_index, stoich) in reaction_stoich.viewitems():
# JSON doesn't allow for integer keys...
metabolite_index = int(str_metabolite_index)
stoichiometric_matrix[metabolite_index, reaction_index] = stoich
duration = 1
# semi-quantitative rate constants
rates = np.full(n_reactions, 10)
system = StochasticSystem(stoichiometric_matrix, rates)
time, counts, events = system.evolve(initial_state, duration)
assert(len(time)-1 == events.sum())
outcome = counts[-1]
difference = (final_state - outcome)
total = np.abs(difference).sum()
print('differences: {}'.format(total))
print('total steps: {}'.format(len(time)))
print(time)
return (time, counts, events)
def test_hang():
# TODO: Use a pytest plug-in to timeout after some threshold.
seed = 807952948
stoich = np_load('stoich.npy')
mol = np_load('complex-counts.npy')
rates = np_load('rates.npy')
system = StochasticSystem(stoich, random_seed=seed)
for i in range(7300):
if i % 100 == 0:
print(i)
result = system.evolve(1, mol, rates)
def test_equilibration():
stoichiometric_matrix = np.array([
[-1, +1, 0],
[+1, -1, -1]])
rates = np.array([10, 10, 0.1])
system = StochasticSystem(stoichiometric_matrix, rates)
state = np.array([1000, 0])
duration = 10
time, counts, events = system.evolve(state, duration)
assert counts[-1].sum() < state.sum()
assert time[-1] <= duration
return (time, counts, events)
def test_dimerization():
stoichiometric_matrix = np.array([
[-1, -2, +1],
[-1, 0, +1],
[+1, 0, -1],
[ 0, +1, 0]])
rates = np.array([3, 1, 1]) * 0.01
system = StochasticSystem(stoichiometric_matrix, rates)
state = np.array([1000, 1000, 0, 0])
duration = 1
time, counts, events = system.evolve(state, duration)
assert time[-1] <= duration
return (time, counts, events)
def test_flagella():
stoichiometry = np.array([[
0, 0, 0, 0, 0, -4, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0
],
[
-26, -34, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0
],
[
0, 0, 0, -1, -1, 0, 0, -1, -1, -1, -1, -1,
-6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -12,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, -1, 1, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0,
0, 0, 0, 0, -1, 0, 0, 0, 1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -5,
-120, 0, 0, 1, 0, -1, -1
]])
substrate = np.array([
21, 1369, 69, 4, 1, 1674, 0, 48, 53, 49, 61, 7, 3, 28, 151, 0, 26, 20,
3, 3, 9, 16, 7, 26, 280, 26, 64, 67, 310, 13559, 208, 16, 22, 0, 17, 0
], np.int64)
rates = np.array([1.e-05, 1.e-05, 1.e-05, 1.e-05, 1.e-05, 1.e-05])
arrow = StochasticSystem(stoichiometry)
result = arrow.evolve(1.0, substrate, rates)
print('flagella result: {}'.format(result))
def test_obsidian():
stoichiometric_matrix = np.array(
[[1, 1, -1, 0], [-2, 0, 0, 1], [-1, -1, 1, 0]], np.int64)
rates = np.array([3, 1, 1]) * 0.01
arrow = StochasticSystem(stoichiometric_matrix)
result = arrow.evolve(1.0, np.array([50, 20, 30, 40], np.int64), rates)
print('steps: {}'.format(result['steps']))
print('time: {}'.format(result['time']))
print('events: {}'.format(result['events']))
print('occurrences: {}'.format(result['occurrences']))
print('outcome: {}'.format(result['outcome']))
assert (arrow.obsidian.reactions_count() == stoichiometric_matrix.shape[0])
assert (
arrow.obsidian.substrates_count() == stoichiometric_matrix.shape[1])
return result
def test_pickle():
stoichiometric_matrix = np.array(
[[1, 1, -1, 0], [-2, 0, 0, 1], [-1, -1, 1, 0]], np.int64)
rates = np.array([3, 1, 1]) * 0.01
arrow = StochasticSystem(stoichiometric_matrix)
pickled_arrow = pickle.dumps(arrow)
unpickled_arrow = pickle.loads(pickled_arrow)
result = arrow.evolve(1.0, np.array([50, 20, 30, 40], np.int64), rates)
straight = test_obsidian()
assert (result['steps'] == straight['steps'])
assert ((result['time'] == straight['time']).all())
assert ((result['events'] == straight['events']).all())
assert ((result['occurrences'] == straight['occurrences']).all())
assert ((result['outcome'] == straight['outcome']).all())
print('arrow object pickled is {} bytes'.format(len(pickled_arrow)))
def test_compare_runtime():
stoichiometric_matrix, rates, initial_state, final_state = load_complexation(
)
duration = 1
amplify = 100
reference = GillespieReference(stoichiometric_matrix)
reference_start = seconds_since_epoch()
for i in range(amplify):
result = reference.evolve(duration, initial_state, rates)
reference_end = seconds_since_epoch()
system = StochasticSystem(stoichiometric_matrix)
obsidian_start = seconds_since_epoch()
for i in range(amplify):
result = system.evolve(duration, initial_state, rates)
obsidian_end = seconds_since_epoch()
print('reference Python implementation elapsed seconds: {}'.format(
reference_end - reference_start))
print(
'obsidian C implementation elapsed seconds: {}'.format(obsidian_end -
obsidian_start))
def __init__(self, initial_parameters=None):
if not initial_parameters:
initial_parameters = {}
super(Complexation, self).__init__(initial_parameters)
self.derive_defaults('stoichiometry', 'reaction_ids', keys_list)
self.monomer_ids = self.parameters['monomer_ids']
self.complex_ids = self.parameters['complex_ids']
self.reaction_ids = self.parameters['reaction_ids']
self.stoichiometry = self.parameters['stoichiometry']
self.rates = self.parameters['rates']
self.complexation_stoichiometry, self.complexation_rates = build_complexation_stoichiometry(
self.stoichiometry, self.rates, self.reaction_ids,
self.monomer_ids, self.complex_ids)
self.complexation = StochasticSystem(self.complexation_stoichiometry)
self.mass_deriver_key = self.or_default(initial_parameters,
'mass_deriver_key')
def test_get_set_random_state():
stoich = np.array([[1, 1, -1, 0], [-2, 0, 0, 1], [-1, -1, 1, 0]])
system = StochasticSystem(stoich)
state = np.array([1000, 1000, 0, 0])
rates = np.array([3.0, 1.0, 1.0])
system.evolve(1, state, rates)
rand_state = system.obsidian.get_random_state()
result_1 = system.evolve(1, state, rates)
result_2 = system.evolve(1, state, rates)
with np.testing.assert_raises(AssertionError):
for key in ('time', 'events', 'occurrences', 'outcome'):
np.testing.assert_array_equal(result_1[key], result_2[key])
system.obsidian.set_random_state(*rand_state)
result_1_again = system.evolve(1, state, rates)
for key in ('time', 'events', 'occurrences', 'outcome'):
np.testing.assert_array_equal(result_1[key], result_1_again[key])
class Translation(Process):
name = 'translation'
defaults = {
'sequences': {
('oA', 'eA'): A,
('oAZ', 'eA'): A,
('oAZ', 'eZ'): Z,
('oB', 'eB'): B,
('oBY', 'eB'): B,
('oBY', 'eY'): Y},
'templates': {
('oA', 'eA'): generate_template(('oA', 'eA'), 20, ['eA']),
('oAZ', 'eA'): generate_template(('oAZ', 'eA'), 20, ['eA']),
('oAZ', 'eZ'): generate_template(('oAZ', 'eZ'), 60, ['eZ']),
('oB', 'eB'): generate_template(('oB', 'eB'), 30, ['eB']),
('oBY', 'eB'): generate_template(('oBY', 'eB'), 30, ['eB']),
('oBY', 'eY'): generate_template(('oBY', 'eY'), 40, ['eY'])},
'transcript_affinities': {
('oA', 'eA'): 1.0,
('oAZ', 'eA'): 2.0,
('oAZ', 'eZ'): 5.0,
('oB', 'eB'): 1.0,
('oBY', 'eB'): 2.0,
('oBY', 'eY'): 5.0},
'elongation_rate': 5.0,
'polymerase_occlusion': 10,
'symbol_to_monomer': amino_acids,
'monomer_ids': monomer_ids,
'concentration_keys': [],
'mass_deriver_key': 'mass_deriver',
'concentrations_deriver_key': 'translation_concentrations',
'time_step': 1.0,
}
def __init__(self, initial_parameters=None):
'''A stochastic translation 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 ArcA, you must refer to the gene, transcript, and
protein each as acrA.
.. DANGER::
This documentation will need to be updated to reflect the
changes in `#185
<https://github.com/CovertLab/vivarium/pull/185>`_
:term:`Ports`:
* **ribosomes**: Expects the ``ribosomes`` variable, whose
value is a list of the configurations of the ribosomes
currently active.
* **molecules**: Expects variables for each of the RNA
nucleotides.
* **transcripts**: Expects variables for each transcript to
translate. Translation will read transcripts from this port.
* **proteins**: Expects variables for each protein product. The
produced proteins will be added to this port as counts.
* **concentrations**: Expects variables for each key in
``concentration_keys``. This will be used by a :term:`deriver`
to convert counts to concentrations.
Arguments:
initial_parameters: A dictionary of configuration options.
Accepts the following keys:
* **sequences** (:py:class:`dict`): Maps from operon
name to the RNA sequence of the operon, as a
:py:class:`str`.
* **templates** (:py:class:`dict`): Maps from the name
of an transcript to a :term:`template specification`.
The template specification may be generated by
:py:func:`cell.library.polymerize.generate_template`
like so:
>>> from vivarium_cell.library.polymerize import (
... generate_template)
>>> from vivarium.library.pretty import format_dict
>>> terminator_index = 5
>>> template = generate_template(
... 'oA', terminator_index, ['product1'])
>>> print(format_dict(template))
{
"direction": 1,
"id": "oA",
"position": 0,
"sites": [],
"terminators": [
{
"position": 5,
"products": [
"product1"
],
"strength": 1.0
}
]
}
* **transcript_affinities** (:py:class:`dict`): A map
from the name of a transcript to the binding affinity
(a :py:class:`float`) of the ribosome for the
transcript.
* **elongation_rate** (:py:class:`float`): The
elongation rate of the ribosome.
.. todo:: Units of elongation rate
* **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:`cell.data.amino_acids.amino_acids`.
* **monomer_ids** (:py:class:`list`): A list of the
names of the free monomers consumed by translation.
This can generally be computed as:
>>> import pprint
>>>
>>> from vivarium_cell.data.amino_acids import amino_acids
>>> monomer_ids = amino_acids.values()
>>> pp = pprint.PrettyPrinter()
>>> pp.pprint(list(monomer_ids))
['Alanine',
'Arginine',
'Asparagine',
'Aspartate',
'Cysteine',
'Glutamate',
'Glutamine',
'Glycine',
'Histidine',
'Isoleucine',
'Leucine',
'Lysine',
'Methionine',
'Phenylalanine',
'Proline',
'Serine',
'Threonine',
'Tryptophan',
'Tyrosine',
'Valine']
Note that we only included the `list()` transformation
to make the output prettier. The `dict_values` object
returned by `.values()` is sufficiently list-like for
use here. Also note that :py:mod:`pprint` just makes
the output prettier.
* **concentration_keys** (:py:class:`list`): A list of
variables you want to be able to access as
concentrations from the *concentrations* port. The
actual conversion is handled by a deriver.
Example configuring the process (uses
:py:func:vivarium.library.pretty.format_dict):
>>> from vivarium.library.pretty import format_dict
>>> from vivarium_cell.data.amino_acids import amino_acids
>>> from vivarium_cell.library.polymerize import generate_template
>>> random.seed(0) # Needed because process is stochastic
>>> np.random.seed(0)
>>> configurations = {
... 'sequences': {
... ('oA', 'eA'): 'AWDPT',
... ('oAZ', 'eZ'): 'YVEGELENGGMFISC',
... },
... 'templates': {
... ('oA', 'eA'): generate_template(('oA', 'eA'), 5, ['eA']),
... ('oAZ', 'eZ'): generate_template(('oAZ', 'eZ'), 15, ['eA', 'eZ']),
... },
... 'transcript_affinities': {
... ('oA', 'eA'): 1.0,
... ('oAZ', 'eZ'): 1.0,
... },
... 'elongation_rate': 10.0,
... 'polymerase_occlusion': 10,
... 'symbol_to_monomer': amino_acids,
... 'monomer_ids': amino_acids.values(),
... 'concentration_keys': []
... }
>>> # make the translation process, and initialize the states
>>> translation = Translation(configurations) # doctest:+ELLIPSIS
>>> states = {
... 'ribosomes': {},
... 'molecules': {},
... 'proteins': {UNBOUND_RIBOSOME_KEY: 2},
... 'transcripts': {
... 'oA': 10,
... 'oAZ': 10,
... }
... }
>>> states['molecules'].update(
... {
... molecule_id: 100
... for molecule_id in translation.monomer_ids
... }
... )
>>> update = translation.next_update(1, states)
>>> print(update['ribosomes'])
{'_add': [{'path': (1,), 'state': <class 'vivarium_cell.processes.translation.Ribosome'>: {'id': 1, 'state': 'occluding', 'position': 9, 'template': ('oAZ', 'eZ'), 'template_index': 0, 'terminator': 0}}, {'path': (2,), 'state': <class 'vivarium_cell.processes.translation.Ribosome'>: {'id': 2, 'state': 'occluding', 'position': 9, 'template': ('oAZ', 'eZ'), 'template_index': 0, 'terminator': 0}}], '_delete': []}
'''
if not initial_parameters:
initial_parameters = {}
self.monomer_symbols = list(amino_acids.keys())
self.monomer_ids = list(amino_acids.values())
self.default_parameters = copy.deepcopy(self.defaults)
templates = self.or_default(initial_parameters, 'templates')
self.default_parameters['protein_ids'] = all_products({
key: Template(config)
for key, config in templates.items()})
self.default_parameters['transcript_order'] = list(
initial_parameters.get(
'transcript_affinities',
self.default_parameters['transcript_affinities']).keys())
self.default_parameters['molecule_ids'] = self.monomer_ids
self.parameters = copy.deepcopy(self.default_parameters)
self.parameters.update(initial_parameters)
self.sequences = self.parameters['sequences']
self.templates = self.parameters['templates']
self.transcript_affinities = self.parameters['transcript_affinities']
self.operons = gather_genes(self.transcript_affinities)
self.operon_order = list(self.operons.keys())
self.transcript_order = self.parameters['transcript_order']
self.transcript_count = len(self.transcript_order)
self.monomer_ids = self.parameters['monomer_ids']
self.molecule_ids = self.parameters['molecule_ids']
self.molecule_ids.extend(['ATP', 'ADP'])
self.protein_ids = self.parameters['protein_ids']
self.symbol_to_monomer = self.parameters['symbol_to_monomer']
self.elongation = 0
self.elongation_rate = self.parameters['elongation_rate']
self.polymerase_occlusion = self.parameters['polymerase_occlusion']
self.concentration_keys = self.parameters['concentration_keys']
self.affinity_vector = np.array([
self.transcript_affinities[transcript_key]
for transcript_key in self.transcript_order], dtype=np.float64)
self.stoichiometry = build_stoichiometry(self.transcript_count)
self.initiation = StochasticSystem(self.stoichiometry)
self.ribosome_id = 0
self.protein_keys = self.concentration_keys + self.protein_ids
self.all_protein_keys = self.protein_keys + [UNBOUND_RIBOSOME_KEY]
self.mass_deriver_key = self.or_default(initial_parameters, 'mass_deriver_key')
self.concentrations_deriver_key = self.or_default(
initial_parameters, 'concentrations_deriver_key')
log.info('translation parameters: {}'.format(self.parameters))
super(Translation, self).__init__(self.parameters)
def ports_schema(self):
def add_mass(schema, masses, key):
if '_properties' not in schema:
schema['_properties'] = {}
if key in masses:
schema['_properties']['mw'] = masses[key]
return schema
return {
'ribosomes': {
'*': {
'id': {
'_default': -1,
'_updater': 'set'},
'domain': {
'_default': 0,
'_updater': 'set'},
'state': {
'_default': None,
'_updater': 'set',
'_emit': True},
'position': {
'_default': 0,
'_updater': 'set',
'_emit': True},
'template': {
'_default': None,
'_updater': 'set',
'_emit': True},
'template_index': {
'_default': 0,
'_updater': 'set',
'_emit': True}}},
'global': {},
'molecules': {
molecule: add_mass({
'_emit': True,
'_default': 0,
'_divider': 'split'}, molecular_weight, molecule)
for molecule in self.molecule_ids},
'transcripts': {
transcript: add_mass({
'_default': 0,
'_divider': 'split'}, molecular_weight, transcript)
for transcript in list(self.operons.keys())},
'proteins': {
protein: add_mass({
'_default': 0,
'_divider': 'split',
'_emit': True}, molecular_weight, protein)
for protein in self.all_protein_keys},
'concentrations': {
molecule: {
'_default': 0.0,
'_updater': 'set'}
for molecule in self.protein_keys}}
def derivers(self):
return {
self.mass_deriver_key: {
'deriver': 'mass_deriver',
'port_mapping': {
'global': 'global'}},
self.concentrations_deriver_key: {
'deriver': 'concentrations_deriver',
'port_mapping': {
'global': 'global',
'counts': 'proteins',
'concentrations': 'concentrations'},
'config': {
'concentration_keys': self.protein_keys}}}
def next_update(self, timestep, states):
molecules = states['molecules']
transcripts = states['transcripts']
proteins = states['proteins']
ribosomes = {
id: Ribosome(ribosome)
for id, ribosome in states['ribosomes'].items()}
original_ribosome_keys = ribosomes.keys()
gene_counts = np.array(
list(transcripts_to_gene_counts(transcripts, self.operons).values()),
dtype=np.int64)
# Find out how many transcripts are currently blocked by a
# newly initiated ribosome
bound_transcripts = np.zeros(self.transcript_count, dtype=np.int64)
ribosomes_by_transcript = {
transcript_key: []
for transcript_key in self.transcript_order}
for ribosome in ribosomes.values():
ribosomes_by_transcript[ribosome.template].append(ribosome)
for index, transcript in enumerate(self.transcript_order):
bound_transcripts[index] = len([
ribosome
for ribosome in ribosomes_by_transcript[transcript]
if ribosome.is_bound()])
# Make the state for a gillespie simulation out of total number of each
# transcript not blocked by a bound ribosome, concatenated with the number
# of each transcript that is bound by a ribosome.
# These are the two states for each transcript the simulation
# will operate on, essentially going back and forth between
# bound and unbound states.
original_unbound_ribosomes = proteins[UNBOUND_RIBOSOME_KEY]
monomer_limits = {
monomer: molecules[monomer]
for monomer in self.monomer_ids}
unbound_ribosomes = original_unbound_ribosomes
templates = {
key: Template(template)
for key, template in self.templates.items()}
time = 0
now = 0
elongation = Elongation(
self.sequences,
templates,
monomer_limits,
self.symbol_to_monomer,
self.elongation)
while time < timestep:
# build the state vector for the gillespie simulation
substrate = np.concatenate([
gene_counts - bound_transcripts,
bound_transcripts,
[unbound_ribosomes]])
# find number of monomers until next terminator
distance = 1 / self.elongation_rate
# 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, ribosomes = elongation.step(
interval,
monomer_limits,
ribosomes)
unbound_ribosomes += terminations
else:
elongation.store_partial(interval)
terminations = 0
# run simulation for interval of time to next terminator
result = self.initiation.evolve(
interval,
substrate,
self.affinity_vector)
# go through each event in the simulation and update the state
ribosome_bindings = 0
for now, event in zip(result['time'], result['events']):
# ribosome has bound the transcript
transcript_key = self.transcript_order[event]
bound_transcripts[event] += 1
self.ribosome_id += 1
new_ribosome = Ribosome({
'id': self.ribosome_id,
'template': transcript_key,
'position': 0})
new_ribosome.bind()
new_ribosome.start_polymerizing()
ribosomes[new_ribosome.id] = new_ribosome
ribosome_bindings += 1
unbound_ribosomes -= 1
# deal with occluding rnap
for ribosome in ribosomes.values():
if ribosome.is_unoccluding(self.polymerase_occlusion):
bound_transcripts[ribosome.template_index] -= 1
ribosome.unocclude()
time += interval
# track how far elongation proceeded to start from next iteration
self.elongation = elongation.elongation - int(elongation.elongation)
proteins = {
UNBOUND_RIBOSOME_KEY: unbound_ribosomes - original_unbound_ribosomes}
proteins.update(elongation.complete_polymers)
molecules = {
key: count * -1
for key, count in elongation.monomers.items()}
original = set(original_ribosome_keys)
current = set(ribosomes.keys())
bound_ribosomes = current - original
completed_ribosomes = original - current
continuing_ribosomes = original - completed_ribosomes
# ATP hydrolysis cost is 2 per amino acid elongation
molecules['ATP'] = 0
molecules['ADP'] = 0
for count in elongation.monomers.values():
molecules['ATP'] -= 2 * count
molecules['ADP'] += 2 * count
ribosome_updates = {
id: ribosomes[id]
for id in continuing_ribosomes}
add_ribosomes = [
{'path': (bound,), 'state': ribosomes[bound]}
for bound in bound_ribosomes]
delete_ribosomes = [
(completed,)
for completed in completed_ribosomes]
ribosome_updates['_add'] = add_ribosomes
ribosome_updates['_delete'] = delete_ribosomes
update = {
'ribosomes': ribosome_updates,
'molecules': molecules,
'proteins': proteins}
return update
def __init__(self, initial_parameters=None):
'''A stochastic translation 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 ArcA, you must refer to the gene, transcript, and
protein each as acrA.
.. DANGER::
This documentation will need to be updated to reflect the
changes in `#185
<https://github.com/CovertLab/vivarium/pull/185>`_
:term:`Ports`:
* **ribosomes**: Expects the ``ribosomes`` variable, whose
value is a list of the configurations of the ribosomes
currently active.
* **molecules**: Expects variables for each of the RNA
nucleotides.
* **transcripts**: Expects variables for each transcript to
translate. Translation will read transcripts from this port.
* **proteins**: Expects variables for each protein product. The
produced proteins will be added to this port as counts.
* **concentrations**: Expects variables for each key in
``concentration_keys``. This will be used by a :term:`deriver`
to convert counts to concentrations.
Arguments:
initial_parameters: A dictionary of configuration options.
Accepts the following keys:
* **sequences** (:py:class:`dict`): Maps from operon
name to the RNA sequence of the operon, as a
:py:class:`str`.
* **templates** (:py:class:`dict`): Maps from the name
of an transcript to a :term:`template specification`.
The template specification may be generated by
:py:func:`cell.library.polymerize.generate_template`
like so:
>>> from vivarium_cell.library.polymerize import (
... generate_template)
>>> from vivarium.library.pretty import format_dict
>>> terminator_index = 5
>>> template = generate_template(
... 'oA', terminator_index, ['product1'])
>>> print(format_dict(template))
{
"direction": 1,
"id": "oA",
"position": 0,
"sites": [],
"terminators": [
{
"position": 5,
"products": [
"product1"
],
"strength": 1.0
}
]
}
* **transcript_affinities** (:py:class:`dict`): A map
from the name of a transcript to the binding affinity
(a :py:class:`float`) of the ribosome for the
transcript.
* **elongation_rate** (:py:class:`float`): The
elongation rate of the ribosome.
.. todo:: Units of elongation rate
* **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:`cell.data.amino_acids.amino_acids`.
* **monomer_ids** (:py:class:`list`): A list of the
names of the free monomers consumed by translation.
This can generally be computed as:
>>> import pprint
>>>
>>> from vivarium_cell.data.amino_acids import amino_acids
>>> monomer_ids = amino_acids.values()
>>> pp = pprint.PrettyPrinter()
>>> pp.pprint(list(monomer_ids))
['Alanine',
'Arginine',
'Asparagine',
'Aspartate',
'Cysteine',
'Glutamate',
'Glutamine',
'Glycine',
'Histidine',
'Isoleucine',
'Leucine',
'Lysine',
'Methionine',
'Phenylalanine',
'Proline',
'Serine',
'Threonine',
'Tryptophan',
'Tyrosine',
'Valine']
Note that we only included the `list()` transformation
to make the output prettier. The `dict_values` object
returned by `.values()` is sufficiently list-like for
use here. Also note that :py:mod:`pprint` just makes
the output prettier.
* **concentration_keys** (:py:class:`list`): A list of
variables you want to be able to access as
concentrations from the *concentrations* port. The
actual conversion is handled by a deriver.
Example configuring the process (uses
:py:func:vivarium.library.pretty.format_dict):
>>> from vivarium.library.pretty import format_dict
>>> from vivarium_cell.data.amino_acids import amino_acids
>>> from vivarium_cell.library.polymerize import generate_template
>>> random.seed(0) # Needed because process is stochastic
>>> np.random.seed(0)
>>> configurations = {
... 'sequences': {
... ('oA', 'eA'): 'AWDPT',
... ('oAZ', 'eZ'): 'YVEGELENGGMFISC',
... },
... 'templates': {
... ('oA', 'eA'): generate_template(('oA', 'eA'), 5, ['eA']),
... ('oAZ', 'eZ'): generate_template(('oAZ', 'eZ'), 15, ['eA', 'eZ']),
... },
... 'transcript_affinities': {
... ('oA', 'eA'): 1.0,
... ('oAZ', 'eZ'): 1.0,
... },
... 'elongation_rate': 10.0,
... 'polymerase_occlusion': 10,
... 'symbol_to_monomer': amino_acids,
... 'monomer_ids': amino_acids.values(),
... 'concentration_keys': []
... }
>>> # make the translation process, and initialize the states
>>> translation = Translation(configurations) # doctest:+ELLIPSIS
>>> states = {
... 'ribosomes': {},
... 'molecules': {},
... 'proteins': {UNBOUND_RIBOSOME_KEY: 2},
... 'transcripts': {
... 'oA': 10,
... 'oAZ': 10,
... }
... }
>>> states['molecules'].update(
... {
... molecule_id: 100
... for molecule_id in translation.monomer_ids
... }
... )
>>> update = translation.next_update(1, states)
>>> print(update['ribosomes'])
{'_add': [{'path': (1,), 'state': <class 'vivarium_cell.processes.translation.Ribosome'>: {'id': 1, 'state': 'occluding', 'position': 9, 'template': ('oAZ', 'eZ'), 'template_index': 0, 'terminator': 0}}, {'path': (2,), 'state': <class 'vivarium_cell.processes.translation.Ribosome'>: {'id': 2, 'state': 'occluding', 'position': 9, 'template': ('oAZ', 'eZ'), 'template_index': 0, 'terminator': 0}}], '_delete': []}
'''
if not initial_parameters:
initial_parameters = {}
self.monomer_symbols = list(amino_acids.keys())
self.monomer_ids = list(amino_acids.values())
self.default_parameters = copy.deepcopy(self.defaults)
templates = self.or_default(initial_parameters, 'templates')
self.default_parameters['protein_ids'] = all_products({
key: Template(config)
for key, config in templates.items()})
self.default_parameters['transcript_order'] = list(
initial_parameters.get(
'transcript_affinities',
self.default_parameters['transcript_affinities']).keys())
self.default_parameters['molecule_ids'] = self.monomer_ids
self.parameters = copy.deepcopy(self.default_parameters)
self.parameters.update(initial_parameters)
self.sequences = self.parameters['sequences']
self.templates = self.parameters['templates']
self.transcript_affinities = self.parameters['transcript_affinities']
self.operons = gather_genes(self.transcript_affinities)
self.operon_order = list(self.operons.keys())
self.transcript_order = self.parameters['transcript_order']
self.transcript_count = len(self.transcript_order)
self.monomer_ids = self.parameters['monomer_ids']
self.molecule_ids = self.parameters['molecule_ids']
self.molecule_ids.extend(['ATP', 'ADP'])
self.protein_ids = self.parameters['protein_ids']
self.symbol_to_monomer = self.parameters['symbol_to_monomer']
self.elongation = 0
self.elongation_rate = self.parameters['elongation_rate']
self.polymerase_occlusion = self.parameters['polymerase_occlusion']
self.concentration_keys = self.parameters['concentration_keys']
self.affinity_vector = np.array([
self.transcript_affinities[transcript_key]
for transcript_key in self.transcript_order], dtype=np.float64)
self.stoichiometry = build_stoichiometry(self.transcript_count)
self.initiation = StochasticSystem(self.stoichiometry)
self.ribosome_id = 0
self.protein_keys = self.concentration_keys + self.protein_ids
self.all_protein_keys = self.protein_keys + [UNBOUND_RIBOSOME_KEY]
self.mass_deriver_key = self.or_default(initial_parameters, 'mass_deriver_key')
self.concentrations_deriver_key = self.or_default(
initial_parameters, 'concentrations_deriver_key')
log.info('translation parameters: {}'.format(self.parameters))
super(Translation, self).__init__(self.parameters)
class Complexation(Process):
defaults = {
'monomer_ids': chromosome.complexation_monomer_ids,
'complex_ids': chromosome.complexation_complex_ids,
'stoichiometry': chromosome.complexation_stoichiometry,
'rates': chromosome.complexation_rates,
'mass_deriver_key': 'mass_deriver'
}
def __init__(self, initial_parameters=None):
if not initial_parameters:
initial_parameters = {}
super(Complexation, self).__init__(initial_parameters)
self.derive_defaults('stoichiometry', 'reaction_ids', keys_list)
self.monomer_ids = self.parameters['monomer_ids']
self.complex_ids = self.parameters['complex_ids']
self.reaction_ids = self.parameters['reaction_ids']
self.stoichiometry = self.parameters['stoichiometry']
self.rates = self.parameters['rates']
self.complexation_stoichiometry, self.complexation_rates = build_complexation_stoichiometry(
self.stoichiometry, self.rates, self.reaction_ids,
self.monomer_ids, self.complex_ids)
self.complexation = StochasticSystem(self.complexation_stoichiometry)
self.mass_deriver_key = self.or_default(initial_parameters,
'mass_deriver_key')
def ports_schema(self):
return {
'monomers': {
monomer: {
'_default': 0,
'_emit': True,
'_properties': {
'mw': molecular_weight[monomer]
} if monomer in molecular_weight else {}
}
for monomer in self.monomer_ids
},
'complexes': {
complex: {
'_default': 0,
'_emit': True,
'_properties': {
'mw': molecular_weight[complex]
} if complex in molecular_weight else {}
}
for complex in self.complex_ids
},
'global': {}
}
def derivers(self):
return {
self.mass_deriver_key: {
'deriver': 'mass',
'port_mapping': {
'global': 'global'
}
}
}
def next_update(self, timestep, states):
monomers = states['monomers']
complexes = states['complexes']
substrate = np.zeros(len(self.monomer_ids) + len(self.complex_ids),
dtype=np.int64)
for index, monomer_id in enumerate(self.monomer_ids):
substrate[index] = monomers[monomer_id]
for index, complex_id in enumerate(self.complex_ids):
substrate[index + len(self.monomer_ids)] = complexes[complex_id]
result = self.complexation.evolve(timestep, substrate,
self.complexation_rates)
outcome = result['outcome'] - substrate
monomers_update = {
monomer_id: outcome[index]
for index, monomer_id in enumerate(self.monomer_ids)
}
complexes_update = {
complex_id: outcome[index + len(self.monomer_ids)]
for index, complex_id in enumerate(self.complex_ids)
}
update = {'monomers': monomers_update, 'complexes': complexes_update}
return update
class Transcription(Process):
name = 'transcription'
defaults = {
'promoter_affinities': {},
'transcription_factors': [],
'sequence': '',
'templates': {},
'genes': {},
'elongation_rate': 1.0,
'polymerase_occlusion': 5,
'symbol_to_monomer': nucleotides,
'monomer_ids': monomer_ids,
'concentrations_deriver_key': 'transcription_concentrations',
'initial_domains': {
0: {
'id': 0,
'lead': 0,
'lag': 0,
'children': []
}
},
'molecule_ids': monomer_ids,
'time_step': 1.0,
}
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 build_affinity_vector(self, promoters, factors):
vector = np.zeros(len(self.promoter_order), dtype=np.float64)
for index, promoter_key in enumerate(self.promoter_order):
promoter = promoters[promoter_key]
binding = promoter.binding_state(factors)
affinity = self.promoter_affinities.get(binding, 0.0)
# print('promoter state - {}: {}'.format(binding, affinity))
vector[index] = affinity
return vector
def chromosome_config(self, chromosome_states):
return dict(chromosome_states,
sequence=self.sequence,
promoters=self.templates,
promoter_order=self.promoter_order,
genes=self.genes)
def ports_schema(self):
schema = {}
schema['chromosome'] = {
'rnap_id': {
'_default': 1,
'_updater': 'set'
},
'root_domain': {
'_default': 0,
'_updater': 'set'
},
'domains': {
'*': {
'id': {
'_default': 1,
'_updater': 'set'
},
'lead': {
'_default': 0,
'_updater': 'set'
},
'lag': {
'_default': 0,
'_updater': 'set'
},
'children': {
'_default': [],
'_updater': 'set'
}
}
},
'rnaps': {
'*': {
'id': {
'_default': -1,
'_updater': 'set'
},
'domain': {
'_default': 0,
'_updater': 'set'
},
'state': {
'_default': None,
'_updater': 'set',
'_emit': True
},
'position': {
'_default': 0,
'_updater': 'set',
'_emit': True
},
'template': {
'_default': None,
'_updater': 'set',
'_emit': True
},
'template_index': {
'_default': 0,
'_updater': 'set',
'_emit': True
},
'terminator': {
'_default': 0,
'_updater': 'set',
'_emit': True
}
}
}
}
initial_domains = {
id: {
'id': {
'_default': id,
'_updater': 'set'
},
'lead': {
'_default': 0,
'_updater': 'set'
},
'lag': {
'_default': 0,
'_updater': 'set'
},
'children': {
'_default': [],
'_updater': 'set'
}
}
for id, domain in self.initial_domains.items()
}
schema['chromosome']['domains'].update(initial_domains)
schema['molecules'] = {
molecule: {
'_default': 0,
'_divider': 'split',
'_emit': True
}
for molecule in self.molecule_ids
}
schema['factors'] = {
factor: {
'_default': 0.0,
'_divider': 'split'
}
for factor in self.transcription_factors
}
schema['transcripts'] = {
protein: {
'_default': 0,
'_divider': 'split',
'_emit': True
}
for protein in self.transcript_ids
}
schema['proteins'] = {
protein: {
'_default': 0,
'_divider': 'split',
'_emit': True
}
for protein in self.protein_ids
}
schema['global'] = {}
return schema
def derivers(self):
return {
self.concentrations_deriver_key: {
'deriver': 'concentrations_deriver',
'port_mapping': {
'global': 'global',
'counts': 'proteins',
'concentrations': 'factors'
},
'config': {
'concentration_keys': self.transcription_factors
}
}
}
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, 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))
import json
from arrow import StochasticSystem
import numpy as np
duration = 2**31
with open('data/complexation/large-initial.json') as f:
data = json.load(f)
stoich = np.array(data['stoich'])
rates = np.array(data['rates']) * 1e-30
counts = np.array(data['counts'])
system = StochasticSystem(stoich.T, random_seed=0)
while True:
result = system.evolve(duration, counts, rates)
updated_counts = result['outcome']
if not np.any(counts - updated_counts):
break
if np.any(updated_counts < 0):
raise Exception('Negative counts')
counts = updated_counts
print(counts)