def new_random_Methyl(self, S):
"""
Creates a methylation with random parameters.
Args:
S: Species to methylate
Returns:
[methyl_inter,S_methyl]:returns a Methyl interaction and a methylated species.
"""
parameters = {}
if S.isinstance("TF"):
parameters['TF'] = self.Random.random() * 2
for tt in S.types:
if tt not in ["TF", "Methylable", "Input", "Output"]:
parameters[tt] = [
mutation.sample_dictionary_ranges('Species.{}'.format(attr),
self.Random)
for attr in S.Tags_Species[tt]
]
# Transform to fit phievo list structure
parameters = [[kk] + val if val else [kk]
for kk, val in parameters.items()]
methyl = mutation.sample_dictionary_ranges('Methyl.methyl', self.Random)
demethyl = mutation.sample_dictionary_ranges('Methyl.demethyl',
self.Random)
return self.new_Methyl(S, methyl, demethyl, parameters)
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 new_random_KPR_Binding(self, L, R):
"""create random KPR_Binding, and return interaction Node and output complex """
list = [[
'Degradable',
mutation.sample_dictionary_ranges('Species.degradation', self.Random)
], ['TF', int(self.Random.random() * 2)], ['Complexable'],
['Phosphorylable']]
a = mutation.sample_dictionary_ranges('KPR_Binding.association',
self.Random)
conc = mutation.sample_dictionary_ranges('KPR_Binding.dissociation',
self.Random)
[KPR_Binding, C] = self.new_KPR_Binding(L, R, a, conc, list)
return [KPR_Binding, C]
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 new_random_Simple_Dephosphorylation(self, K, Sp, S):
""" create new random Simple_Dephosphorylation interaction and return interaction."""
r = mutation.sample_dictionary_ranges('Simple_Dephosphorylation.rate',
self.Random)
D = self.new_Simple_Dephosphorylation(K, Sp, S, r)
return D
def new_Species(self,list,bool_conc = True,manual_conc = 0):
"""Create new Species instance with argument parameters of form [ [], [],.. ], and add to graph """
S=classes_eds2.Species(list)
self.add_Node(S)
if ((S.isinstance('Phosphatase') or S.isinstance('Kinase')) and not S.isinstance('pMHC') ):
if bool_conc:
conc = mutation.sample_dictionary_ranges('Initial_Concentration.concentration',self.Random)
else:
conc = manual_conc
self.new_Initial_Concentration(S,conc)
return S
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
## Create a methylation interaction
from phievo import __silent__, __verbose__
if __verbose__:
print("Execute Methyl (Interaction Template)")
from phievo.Networks import mutation
from phievo.Networks import deriv2
from phievo.Networks import classes_eds2
import copy
## Define the function that assigns new parameters to a new methylable species
mutation.species_types["Methylable"] = lambda random_generator: [
["Methylable"],
[
'Diffusible',
mutation.sample_dictionary_ranges('Species.diffusion', random_generator
)
]
]
classes_eds2.Species.Tags_Species["Methylable"] = []
## Define the default dictionary_range
mutation.dictionary_ranges['Methyl.methyl'] = 0.0 / (mutation.C * mutation.T)
mutation.dictionary_ranges['Methyl.demethyl'] = 0.0 / mutation.T
class Methyl(classes_eds2.Interaction):
"""
Methylation interaction
Args:
Methyl.methyl(float): binding rate of a methyl group
def new_random_Simple_Phosphorylation(self, K, S):
""" Create new random Simple_Phosphorylation interaction and return interaction and product. Note that the inputs must be given (as opposed to random_Simple_Phosphorylation)."""
r = mutation.sample_dictionary_ranges('Simple_Phosphorylation.rate',
self.Random)
[SP, Phospho] = self.new_Simple_Phosphorylation(K, S, r)
return [SP, Phospho]