def main():
# create an instance of Network class:
network = nm.Network(CUR_DIR)
config_dict = network.get_config_dict()
SEED = int(config_dict['SEED'])
USER_SEED = int(config_dict['USER_SEED'])
#load network topology and parameter ranges:
mode = network.process_network(USER_SEED, SEED)
#fname_params_prefix = CUR_DIR + '/cellcycle.'
fname_params_prefix = CUR_DIR + '/repressilator.'
fname_models = CUR_DIR + '/models.txt'
#fname_models = CUR_DIR + '/model_list.5.txt'
#fname_models = CUR_DIR + '/models.bistable.txt'
#import model ids from the input file:
with open(fname_models, 'r') as fh_models:
content = fh_models.read()
models = content.strip().split("\n")
MODELS_TO_INSPECT = list(map(int, models))
#import perturbed params from the params file as an ordered dictionary:
#(parameters are perturbed in an R function)
model_params_dict = ma.import_model_params(fname_params_prefix, \
network, MODELS_TO_INSPECT)
# generate models using perturbed parameters and random ICs:
# (number of random ICs can be set in racipe.cfg)
mg.generate_models_random_ICs(network, model_params_dict)
return None
def main():
# create an instance of Network class:
network = nm.Network(CUR_DIR)
config_dict = network.get_config_dict()
SEED = int(config_dict['SEED'])
USER_SEED = int(config_dict['USER_SEED'])
#load network topology and parameter ranges:
mode = network.process_network(USER_SEED, SEED)
#fname_params_prefix = CUR_DIR + '/cellcycle.'
fname_params_prefix = CUR_DIR + '/emt.'
fname_models = CUR_DIR + '/models.txt'
# name of the file containing steady states and cluster number:
#fname_sstate = fname_params_prefix + 'states.unnormed.txt'
fname_sstate = fname_params_prefix + 'states.clustered.txt'
#import model ids from the input file:
with open(fname_models, 'r') as fh_models:
content = fh_models.read()
models = content.strip().split("\n")
#print(models)
MODELS_TO_INSPECT = list(map(int, models))
#import perturbed params from the params file as an ordered dictionary:
#(parameters are perturbed in an R function)
model_params_dict = ma.import_model_params(fname_params_prefix, \
network, MODELS_TO_INSPECT)
# Import starting state
# ---------------------
CLUSTER_NO = 1
sstate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
# Import starting state
# ---------------------
CLUSTER_NO = 2
estate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
# Generate trajectory of the models from E to M
# --------------------------------------------
#mg.generate_e2m_traj_mpr_signal(network, model_params_dict, sstate_dict)
SIGNALING_NODE_ID = 8
FCHANGE = 2**-8 #2**-9 #2**(-10) #2**(-64) #2**(-32) #2**(-16)- phase.32
mg.generate_traj_decr_sig_gradually(network, model_params_dict, estate_dict, \
SIGNALING_NODE_ID, FCHANGE)
return None
def main():
# create an instance of Network class:
network = nm.Network(CUR_DIR)
config_dict = network.get_config_dict()
SEED = int(config_dict['SEED'])
USER_SEED = int(config_dict['USER_SEED'])
#load network topology and parameter ranges:
mode = network.process_network(USER_SEED, SEED)
#fname_params_prefix = CUR_DIR + '/cellcycle.'
fname_params_prefix = CUR_DIR + '/emt.'
fname_models = CUR_DIR + '/models.txt'
# name of the file containing steady states and cluster number:
#fname_sstate = fname_params_prefix + 'states.unnormed.txt'
fname_sstate = fname_params_prefix + 'states.clustered.txt'
#import model ids from the input file:
with open(fname_models, 'r') as fh_models:
content = fh_models.read()
models = content.strip().split("\n")
#print(models)
MODELS_TO_INSPECT = list(map(int, models))
#import perturbed params from the params file as an ordered dictionary:
#(parameters are perturbed in an R function)
model_params_dict = ma.import_model_params(fname_params_prefix, \
network, MODELS_TO_INSPECT)
#print(MODELS_TO_INSPECT)
#print(len(model_params_dict.keys()))
#print(model_params_dict.keys())
#sys.exit(0)
#for k in model_params_dict.keys():
# print(k)
# print(model_params_dict[k])
# break
# Import starting state
# ---------------------
CLUSTER_NO = 1
sstate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
#print(len(start_state_dict.keys()))
#print(start_state_dict.keys())
#for k in start_state_dict.keys():
# print(k)
# print(np.log2(start_state_dict[k]))
# break
# Import starting state
# ---------------------
CLUSTER_NO = 2
estate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
#print(len(end_state_dict.keys()))
#print(end_state_dict.keys())
#for k in end_state_dict.keys():
# print(k)
# print(np.log2(end_state_dict[k]))
# break
# generate models using perturbed parameters and steady state as IC:
#mg.generate_models_sstate(network, model_params_dict, sstate_dict)
mg.generate_models_signaled_sstate(network, model_params_dict, sstate_dict)
return None
def main():
# create an instance of Network class:
network=nm.Network(CUR_DIR)
config_dict=network.get_config_dict()
SEED=int(config_dict['SEED'])
USER_SEED=int(config_dict['USER_SEED'])
#load network topology and parameter ranges:
mode=network.process_network(USER_SEED, SEED)
#fname_params_prefix = CUR_DIR + '/cellcycle.'
fname_params_prefix = CUR_DIR + '/tswitch.'
fname_models = CUR_DIR + '/models.txt'
# name of the file containing steady states and cluster number:
#fname_sstate = fname_params_prefix + 'states.unnormed.txt'
fname_sstate = fname_params_prefix + 'states.clustered.txt'
#import model ids from the input file:
with open(fname_models, 'r') as fh_models:
content = fh_models.read()
models = content.strip().split("\n")
#print(models)
MODELS_TO_INSPECT = list(map(int, models))
#import perturbed params from the params file as an ordered dictionary:
#(parameters are perturbed in an R function)
model_params_dict = ma.import_model_params(fname_params_prefix, \
network, MODELS_TO_INSPECT)
# Import starting state
# ---------------------
CLUSTER_NO=1
sstate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
# Import starting state
# ---------------------
CLUSTER_NO=2
estate_dict = ma.import_states_byCluster(fname_sstate, \
network, \
MODELS_TO_INSPECT,
CLUSTER_NO)
# Comment about expressions: already antilog transformed
#print(sstate_dict.keys())
#print(sstate_dict.values())
#for k,v in sstate_dict.items():
# print(k)
# print(v)
# break
#sys.exit(0)
# generate trajectory of the models from E to M
# --------------------------------------------
FCHANGE = 2**1.5 # phase 01
SIGNALING_NODE_ID = 2
mg.generate_traj_incr_sig_gradually(network, model_params_dict, sstate_dict, \
SIGNALING_NODE_ID, FCHANGE)
return None