def simulate(sim_mode, **params):
## Setup initialization parameters
peppercorn_params = {'k_fast': 5e-7}
kinda_params = {
'stop_macrostate_mode': sim_mode,
'start_macrostate_mode': 'ordered-complex'
}
## Construct System object
sstats = kinda.from_pil(PILPATH,
peppercorn_params=peppercorn_params,
kinda_params=kinda_params)
## Find relevant resting sets
rs_InputA = sstats.get_restingset(complex_name='InA')
rs_InputB = sstats.get_restingset(complex_name='InB')
rs_OR = sstats.get_restingset(complex_name='OR')
## Get the relevant reactions with the OR gate complex
rxns = [
sstats.get_reaction(reactants=[rs_InputA, rs_OR],
spurious=False,
unproductive=False),
sstats.get_reaction(reactants=[rs_InputB, rs_OR],
spurious=False,
unproductive=False)
]
print "Reaction analysis order:"
for i, rxn in enumerate(rxns):
print i, rxn
rxn_times = []
for rxn in rxns:
print "Analyzing", rxn
start_time = time.time()
## Run simulations
rxn_stats = sstats.get_stats(rxn)
rxn_stats.get_k1(verbose=1, **params)
rxn_stats.get_k2(verbose=1, **params)
end_time = time.time()
print "k1: {} +/- {}".format(rxn_stats.get_k1(max_sims=0),
rxn_stats.get_k1_error(max_sims=0))
print "k2: {} +/- {}".format(rxn_stats.get_k2(max_sims=0),
rxn_stats.get_k2_error(max_sims=0))
rxn_times.append(end_time - start_time)
print "Finished analyzing {} in {} seconds\n".format(
rxn, end_time - start_time)
## Store results
kinda.export_data(sstats, '{}_{}.kinda'.format(OUTPUT_PREFIX, sim_mode))
return sstats, zip(rxns, rxn_times)
c.name for c in restingset.complexes
] # Get all conformation names in the resting set (most of the time there are only 1 or 2)
confs.append(
None
) # None refers to spurious conformations (that aren't similar to any expected conformations)
print "Conformation probabilities for resting set {0}".format(restingset)
for c in confs:
p = rs_stats.get_conformation_prob(c, .025, max_sims=500)
print "\t{0}: {1}%".format(c, p * 100)
## Getting the top 10 MFE structures
mfe_structs = rs_stats.get_top_MFE_structs(10)
print "Top 10 MFE structures for resting set {0}".format(restingset)
for i, s in enumerate(mfe_structs):
print "\t{0}: {1} ({2})".format(i, s[0], s[1])
kinda.export_data(kinda_obj, 'analyze.db')
kinda_obj = kinda.import_data('analyze.db')
# DOES NOT WORK!!!
## Getting the (fractional) reactant depletion due to unproductive reactions
#unproductive_depletion = rs_stats.get_temp_depletion(0.5) # Get depletion with 50% error on any relevant reaction rate
## Getting the rate constant of reactant depletion due to spurious reactions (units: /s)
#spurious_depletion = rs_stats.get_perm_depletion(0.5) # Get depletion with 50% error on any relevant reaction rate
#### To get a system-level score, use the convenience functions in stats_utils.py
#kinda.statistics.stats_utils.calc_unproductive_rxn_score(kinda_obj)
#kinda.statistics.stats_utils.calc_spurious_rxn_score(kinda_obj)
print 'done'
for i, r in enumerate(desired_rxns):
print "\nAnalyzing desired reaction {}: {}".format(i, r)
# Get stats object
rxn_stats = sstats.get_stats(r)
# Query k1 and k2 reaction rates to requested precision
rxn_stats.get_k1(verbose=1, **params)
rxn_stats.get_k2(verbose=1, **params_uni)
print "k1: {} +/- {}".format(rxn_stats.get_k1(max_sims=0),
rxn_stats.get_k1_error(max_sims=0))
print "k2: {} +/- {}".format(rxn_stats.get_k2(max_sims=0),
rxn_stats.get_k2_error(max_sims=0))
## Export all collected data (we'll do it again later, but in case the system crashes...)
kinda.export_data(sstats, DATA_PATH)
## Simulate the most probable leak reactions, with the same accuracy goals. We know they're there, so find them!
for i, r in enumerate(leak_rxns):
print "\nAnalyzing potential leak reaction {}: {}".format(i, r)
# Get stats object
rxn_stats = sstats.get_stats(r)
# Query k1 and k2 reaction rates to requested precision
rxn_stats.get_k1(verbose=1, **params)
rxn_stats.get_k2(verbose=1, **params_uni)
print "k1: {} +/- {}".format(rxn_stats.get_k1(max_sims=0),
rxn_stats.get_k1_error(max_sims=0))
print "k2: {} +/- {}".format(rxn_stats.get_k2(max_sims=0),
