def gillespie_nrm(tspan, c_0, c_poly, reactions, dep_graph):
t = tspan[0]
c = c_0.copy()
T = [t]
C = [c.copy()]
volume = compute_volume(c_0, c_poly)
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand()) / rxn.propensity(c, volume)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
C, T = fire_rxn(rnext, c, t, c_poly, C, T)
while t < tspan[1]:
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand()) / rxn.propensity(c, volume)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
if (rnext.propensity(c, volume) > 0):
C, T = fire_rxn(rnext, c, t, c_poly, C, T)
else:
print(c_poly)
return array(T), array(C)
def gillespie_nrm(tspan, initial_amounts, reactions, dep_graph):
"""
Implementation of the "Next-Reaction Method" variant of the Gillespie
stochastic simulation algorithm, described by Gibson and Bruck.
The main enhancements are:
- Use of dependency graph connecting the reaction channels to prevent
needless rescheduling of reactions that are unaffected by an event.
- Use of an indexed priority queue (pqdict) as a scheduler to achieve
O(log(M)) rescheduling on each iteration, where M is the number of
reactions, assuming the dependency graph is sparse.
The paper describes an additional modification to cut down on the amount of
random number generation which was not implemented here for simplicity.
"""
# initialize state
t = tspan[0]
x = initial_amounts
T = [t]
X = [x]
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand()) / rxn.propensity(x)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append(t)
X.append(x.copy())
# main loop
while t < tspan[1]:
# reschedule
tau = -log(rand()) / rnext.propensity(x)
scheduler[rnext] = t + tau
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand()) / rxn.propensity(x)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append(t)
X.append(x.copy())
return array(T), array(X)
def gillespie_nrm(tspan, initial_amounts, reactions, dep_graph):
"""
Implementation of the "Next-Reaction Method" variant of the Gillespie
stochastic simulation algorithm, described by Gibson and Bruck.
The main enhancements are:
- Use of dependency graph connecting the reaction channels to prevent
needless rescheduling of reactions that are unaffected by an event.
- Use of an indexed priority queue (pqdict) as a scheduler to achieve
O(log(M)) rescheduling on each iteration, where M is the number of
reactions, assuming the dependency graph is sparse.
The paper describes an additional modification to cut down on the amount of
random number generation which was not implemented here for simplicity.
"""
# initialize state
t = tspan[0]
x = initial_amounts
T = [t]
X = [x]
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand())/rxn.propensity(x)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append( t )
X.append( x.copy() )
# main loop
while t < tspan[1]:
# reschedule
tau = -log(rand())/rnext.propensity(x)
scheduler[rnext] = t + tau
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand())/rxn.propensity(x)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append( t )
X.append( x.copy() )
return array(T), array(X)
def test_topitem(self):
# empty
pq = PQDict()
self.assertRaises(KeyError, pq.top)
# non-empty
for num_items in range(1,30):
items = generateData('float', num_items)
pq = PQDict(items)
self.assertEqual(pq.topitem(), min(items, key=lambda x: x[1]))
def gillespie_nrm(tspan, initial_amounts, reactions, dep_graph):
"""
Implementation of the "Next-Reaction Method" variant of the Gillespie
stochastic simulation algorithm, described by Gibson and Bruck.
The main enhancements are:
- Use of dependency graph between reactions to prevent needless
rescheduling of reaction channels that are unaffected by an event.
- Use of an indexed priority queue (pqdict) as a scheduler to achieve
log(N) rescheduling.
The paper describes an additional modification to cut down on the amount of
random number generation which was not implemented here for simplicity.
"""
# initialize state
t = tspan[0]
x = initial_amounts
T = [t]
X = [x]
a = {}
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
a[rxn] = rxn.propensity(x)
tau = -log(rand())/a[rxn]
scheduler[rxn] = t + tau
# first event
rnext, tmin = scheduler.topitem()
t = tmin
x += rnext.stoich
T.append( t )
X.append( x.copy() )
# main loop
while t < tspan[1]:
# reschedule
a[rnext] = rnext.propensity(x)
tau = -log(rand())/a[rnext]
scheduler.updateitem(rnext, t + tau)
for rxn in dep_graph[rnext]:
a_old = a[rxn]
a[rxn] = rxn.propensity(x)
if isfinite(scheduler[rxn]):
tau = (a_old/a[rxn])*(scheduler[rxn] - t)
else:
tau = -log(rand())/a[rxn]
scheduler.updateitem(rxn, t + tau)
rnext, tmin = scheduler.topitem()
# fire!
t = tmin
x += rnext.stoich
T.append( t )
X.append( x.copy() )
return array(T), array(X)
class NrmSubmodel(DynamicSubmodel):
""" Use the Next Reaction Method to predict the dynamics of chemical species in a container
Attributes:
dependencies (:obj:`list` of :obj:`tuple`): dependencies that rate laws have on executed
reactions; entry i provides the indices of reactions whose rate laws depend on reaction i
execution_time_priority_queue (:obj:`PQDict`): NRM indexed priority queue of reactions, with
the earliest scheduled reaction at the front of the queue
propensities (:obj:`np.ndarray`): the most recently calculated propensities of the reactions
modeled by this NRM submodel
random_state (:obj:`np.random.RandomState`): the random state that is shared across the
simulation, which enables reproducible checkpoint and restore of a simulation
"""
# message types sent by NrmSubmodel
SENT_MESSAGE_TYPES = [message_types.ExecuteAndScheduleNrmReaction]
# register the message types sent
messages_sent = SENT_MESSAGE_TYPES
# at any time instant, process messages in this order
MESSAGE_TYPES_BY_PRIORITY = [message_types.ExecuteAndScheduleNrmReaction]
event_handlers = [(message_types.ExecuteAndScheduleNrmReaction, 'handle_ExecuteAndScheduleNrmReaction_msg')]
def __init__(self, id, dynamic_model, reactions, species, dynamic_compartments,
local_species_population, options=None):
""" Initialize an NRM submodel object
Args:
id (:obj:`str`): unique id of this dynamic NRM submodel
dynamic_model (:obj:`DynamicModel`): the aggregate state of a simulation
reactions (:obj:`list` of :obj:`Reaction`): the reactions modeled by this NRM submodel
species (:obj:`list` of :obj:`Species`): the species that participate in the reactions modeled
by this NRM submodel, with their initial concentrations
dynamic_compartments (:obj:`dict`): :obj:`DynamicCompartment`\ s, keyed by id, that contain
species which participate in reactions that this NRM submodel models, including
adjacent compartments used by its transfer reactions
local_species_population (:obj:`LocalSpeciesPopulation`): the store that maintains this
NRM submodel's species population
options (:obj:`dict`, optional): NRM submodel options
Raises:
:obj:`MultialgorithmError`: if the initial NRM wait exponential moving average is not positive
"""
super().__init__(id, dynamic_model, reactions, species, dynamic_compartments,
local_species_population)
self.random_state = RandomStateManager.instance()
self.execution_time_priority_queue = PQDict()
self.options = options
# to enable testing of uninitialized instances auto_initialize controls initialization here
# auto_initialize defaults to True
auto_initialize = True
if options is not None and 'auto_initialize' in options:
auto_initialize = options['auto_initialize']
if auto_initialize:
self.initialize()
def initialize(self):
self.dependencies = self.determine_dependencies()
self.propensities = self.initialize_propensities()
self.initialize_execution_time_priorities()
def determine_dependencies(self):
""" Determine the dependencies that rate laws have on executed reactions
Returns:
:obj:`list` of :obj:`tuple`: entry i provides the indices of reactions whose
rate laws depend on the execution of reaction i
"""
# in a multi-algorithmic simulation, two types of dependencies arise when a reaction executes:
# 1) ones used by NRM: rate laws that use species whose populations are updated by the reaction
# 2) rate laws that use species whose populations might be updated by other submodels
# dependencies[i] will contain the indices of rate laws that depend on reaction i
dependencies = {i: set() for i in range(len(self.reactions))}
# updated_species[i] will contain the ids of species whose populations are updated by reaction i
updated_species = {i: set() for i in range(len(self.reactions))}
# used_species[species_id] will contain the indices of rate laws (rxns) that use species with id species_id
used_species = {species.gen_id(): set() for species in self.species}
# initialize reaction -> species -> reaction dependency dictionaries
for reaction_idx, rxn in enumerate(self.reactions):
net_stoichiometric_coefficients = collections.defaultdict(float)
for participant in rxn.participants:
species_id = participant.species.gen_id()
net_stoichiometric_coefficients[species_id] += participant.coefficient
for species_id, net_stoich_coeff in net_stoichiometric_coefficients.items():
if net_stoich_coeff < 0 or 0 < net_stoich_coeff:
updated_species[reaction_idx].add(species_id)
rate_law = rxn.rate_laws[0]
for species in rate_law.expression.species:
species_id = species.gen_id()
used_species[species_id].add(reaction_idx)
# Sequential case, with one instance each of an SSA, ODE, dFBA submodels:
# NRM must recompute all rate laws that depend on species in a continuous submodels
# get shared species from self.local_species_population
continuously_modeled_species = self.local_species_population.get_continuous_species()
for updated_species_set in updated_species.values():
updated_species_set |= continuously_modeled_species
# Parallel case (to be addressed later), with multiple instances each of an SSA, ODE and dFBA submodels:
# NRM must recompute all rate laws that depend on species shared with any other submodel
# compute reaction to rate laws dependencies
for reaction_idx, rxn in enumerate(self.reactions):
for species_id in updated_species[reaction_idx]:
for rate_law_idx in used_species[species_id]:
dependencies[reaction_idx].add(rate_law_idx)
# convert dependencies into more compact and faster list of tuples
dependencies_list = [None] * len(self.reactions)
for antecedent_rxn, dependent_rxns in dependencies.items():
# to simplify later code remove antecedent_rxn from dependent_rxns
# because antecedent_rxn is handled specially
try:
dependent_rxns.remove(antecedent_rxn)
except KeyError:
pass
dependencies_list[antecedent_rxn] = tuple(dependent_rxns)
return dependencies_list
def initialize_propensities(self):
""" Get the initial propensities of all reactions that have enough species counts to execute
Propensities of reactions with inadequate species counts are set to 0.
Returns:
:obj:`np.ndarray`: the propensities of the reactions modeled by this NRM submodel which
have adequate species counts to execute
"""
propensities = self.calc_reaction_rates()
# avoid reactions with inadequate species counts
enabled_reactions = self.identify_enabled_reactions()
propensities = enabled_reactions * propensities
return propensities
def initialize_execution_time_priorities(self):
""" Initialize the NRM indexed priority queue of reactions
"""
for reaction_idx, propensity in enumerate(self.propensities):
if propensity == 0:
self.execution_time_priority_queue[reaction_idx] = float('inf')
else:
tau = self.random_state.exponential(1.0/propensity) + self.time
self.execution_time_priority_queue[reaction_idx] = tau
def register_nrm_reaction(self, execution_time, reaction_index):
""" Schedule a NRM reaction event with the simulator
Args:
execution_time (:obj:`float`): simulation time at which the reaction will execute
reaction_index (:obj:`int`): index of the reaction to execute
"""
self.send_event_absolute(execution_time, self,
message_types.ExecuteAndScheduleNrmReaction(reaction_index))
def schedule_reaction(self):
""" Schedule the next reaction
"""
next_rxn, next_time = self.execution_time_priority_queue.topitem()
self.register_nrm_reaction(next_time, next_rxn)
def send_initial_events(self):
""" Initialize this NRM submodel and schedule its first reaction
This :obj:`ApplicationSimulationObject` method is called when a :obj:`SimulationEngine` is
initialized.
"""
self.schedule_reaction()
def schedule_next_reaction(self, reaction_index):
""" Schedule the next reaction after a reaction executes
Args:
reaction_index (:obj:`int`): index of the reaction that just executed
"""
# reschedule each reaction whose rate law depends on the execution of reaction reaction_index
# or depends on species whose populations may have been changed by other submodels
for reaction_to_reschedule in self.dependencies[reaction_index]:
# 1. compute new propensity
propensity_new = self.calc_reaction_rate(self.reactions[reaction_to_reschedule])
if propensity_new == 0:
tau_new = float('inf')
else:
# 2. compute new tau from old tau
tau_old = self.execution_time_priority_queue[reaction_to_reschedule]
propensity_old = self.propensities[reaction_to_reschedule]
if propensity_old == 0:
# If propensity_old == 0 then tau_old is infinity, and these cannot be used in
# (propensity_old/propensity_new) * (tau_old - self.time) + self.time
# In this case a new random exponential must be drawn.
# This does not increase the number of random draws, because they're not
# drawn when the propensity is 0.
tau_new = self.random_state.exponential(1.0/propensity_new) + self.time
else:
tau_new = (propensity_old/propensity_new) * (tau_old - self.time) + self.time
self.propensities[reaction_to_reschedule] = propensity_new
# 3. update the reaction's order in the indexed priority queue
self.execution_time_priority_queue[reaction_to_reschedule] = tau_new
# reschedule the reaction that was executed
# 1. compute new propensity
propensity_new = self.calc_reaction_rate(self.reactions[reaction_index])
self.propensities[reaction_index] = propensity_new
# 2. compute new tau
if propensity_new == 0:
tau_new = float('inf')
else:
tau_new = self.random_state.exponential(1.0/propensity_new) + self.time
# 3. update the reaction's order in the indexed priority queue
self.execution_time_priority_queue[reaction_index] = tau_new
# schedule the next reaction
self.schedule_reaction()
def execute_nrm_reaction(self, reaction_index):
""" Execute a reaction now
Args:
reaction_index (:obj:`int`): index of the reaction to execute
"""
self.execute_reaction(self.reactions[reaction_index])
def handle_ExecuteAndScheduleNrmReaction_msg(self, event):
""" Handle an event containing an :obj:`ExecuteAndScheduleNrmReaction` message
Args:
event (:obj:`Event`): a simulation event
"""
# execute the reaction
reaction_index = event.message.reaction_index
self.execute_nrm_reaction(reaction_index)
# schedule the next reaction
self.schedule_next_reaction(reaction_index)