def empty_net():
import random
from phievo.Networks import mutation
seed = int(random.random() * 100000)
g = random.Random(seed)
net = mutation.Mutable_Network(g)
return net
def init_network():
seed = int(random.random() * 100000)
g = random.Random(seed)
L = mutation.Mutable_Network(g)
L.remove_output_when_duplicate = False
parameters = [[
'Degradable',
mutation.sample_dictionary_ranges('Species.degradation', random)
]]
parameters.append(['TF', 1])
parameters.append(['Input', 0])
TF = L.new_Species(parameters)
for k in range(5):
[tm, prom, o1] = L.random_gene('TF')
o1.add_type(['Output', k])
L.activator_required = 1
L.fixed_activity_for_TF = 0
L.write_id()
L.random_Interaction('TFHill')
L.random_Interaction('TFHill')
L.random_Interaction('TFHill')
L.write_id()
for i in L.dict_types['TFHill']:
i.activity = 1
i.threshold = min(0.1, i.threshold)
return L
def init_network():
seed = int(random.random() * 100000)
g = random.Random(seed)
L = mutation.Mutable_Network(g)
L.fixed_activity_for_TF = 0
parameters = [[
'Degradable',
mutation.sample_dictionary_ranges('Species.degradation', random)
]]
parameters.append(['Input', 0])
parameters.append(['Complexable'])
TF1 = L.new_Species(parameters)
parameters = [[
'Degradable',
mutation.sample_dictionary_ranges('Species.degradation', random)
]]
parameters.append(['Input', 1])
parameters.append(['Complexable'])
TF2 = L.new_Species(parameters)
[tm, prom, o1] = L.random_gene('TF')
o1.mutable = False
o1.add_type(['Output', 0])
o1.clean_type('Complexable')
L.write_id()
return L
def init_network():
seed = int(random.random() * 100000)
g = random.Random(seed)
L = mutation.Mutable_Network(g)
parameters = [[
'Degradable',
mutation.sample_dictionary_ranges('Species.degradation', random)
]]
parameters.append(['TF', 1])
parameters.append(['Input', 0])
TF = L.new_Species(parameters)
for k in range(2):
[tm, prom, o1] = L.random_gene('TF')
o1.add_type(['Output', k])
L.activator_required = 1
L.fixed_activity_for_TF = 0
L.write_id()
return L
def init_network():
seed = int(random.random() * 100000)
g = random.Random(seed)
net = mutation.Mutable_Network(g)
## Input
parameters = []
parameters.append(['TF', 1])
parameters.append(['Input', 0])
TF = net.new_Species(parameters)
## Add one output
N_output = 1
for k in range(2):
[tm, prom, o1] = net.random_gene('TF')
o1.add_type(['Output', k])
# for k in range(N_output):
# [tm, prom, o1] = net.random_gene(['TF',1])
# o1.add_type(['Output',k])
net.write_id()
return net
def setUp(self):
seed = 0
g = random.Random(seed)
self.net = mutation.Mutable_Network(g)
def init_network():
seed=int(random.random()*510)
g=random.Random(seed)
L=mutation.Mutable_Network(g)
conc = 1000.0
T = 1.0
# the ligand (the pMHC on the antigen presenting cell)
parameters=[['Ligand']]
parameters.append(['Input',0])
Lig=L.new_Species(parameters)
# the receptor (on the T cell)
parameters=[['Receptor']]
R = L.new_Species(parameters)
# a kinase.
parameters=[['Kinase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
K1 = L.new_Species(parameters)
# a kinase.
parameters=[['Kinase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
K2 = L.new_Species(parameters)
# a kinase.
parameters=[['Kinase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
K3 = L.new_Species(parameters)
# a phosphatase.
parameters=[['Phosphatase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
P1 = L.new_Species(parameters)
# a phosphatase.
parameters=[['Phosphatase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
P2 = L.new_Species(parameters)
# a phosphatase.
parameters=[['Phosphatase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
P3 = L.new_Species(parameters)
# the complex generated: it is a kinase that is phosphorylable and with the label pMHC to indicate it is in the cascade.
parameters=[['Kinase']]
parameters.append(['Phosphorylable'])
parameters.append(['Phospho',0])
parameters.append(['pMHC'])
# the first binding of the receptor and ligand generating the complex.
kappa = 1E-4
[Binding, C] = L.new_KPR_Binding(Lig,R,kappa,parameters)
unbinbing = L.new_KPR_Unbinding(Lig,R,C)
# the output tag placed on the unphosphorylated complex arising from binding of receptor and ligand.
C.add_type(['Output',0])
L.write_id()
# checks consecutive numbering for IO species.
L.verify_IO_numbers()
return L
## Import the libraries from phievo.Networks import mutation,deriv2 import random ### Create an empty network g = random.Random(20160225) # This define a new random number generator L = mutation.Mutable_Network(g) # Create an empty network ### Create a new species _S0_ ## S0 is a reference to access quickly to the newly created species latter in the code. Note that one can add attributes to a species by adding elements to a parameter array that is passed to the new_species method. ## ATTENTION: This way of creating a species is not recommanded as it does not handle the interaction between the network's different species (see next section). It is here as to get a feeling on how the intern code works.</font> parameters=[['Degradable',0.5]] ## The species is degradable with a rate 0.5 parameters.append(['Input',0]) ## The species cannot serve as an input for the evolution algorithm parameters.append(['Complexable']) ## The species can be involved in a complex parameters.append(['Kinase']) ## The specise can phosphorilate another species. parameters.append(['TF',1]) ## 1 for activator 0 for repressor S0 = L.new_Species(parameters) ### Adding a gene ## A species itself is of no use for the evolution algorithm. The architecture of a networks associates a TModule and a CorePromoter to a a species to build an cluster representing a gene for the program. The TModule is there so that other transcription factors can bind to it and regulate S0. L = mutation.Mutable_Network(g) ## Clear the network ## Gene 0 parameters=[['Degradable',0.5]] parameters.append(['TF',1]) parameters.append(['Complexable']) TM0,prom0,S0 = L.new_gene(0.5,5,parameters) ## Adding a new gene creates a TModule, a CorePromoter and a species