KarrLab/wc_rules

from .graphs import build_Lyn, build_Syk, build_receptor, recruit_to_receptor, recruit_to_ligand
from .templates import TransPhosphorylationModel

from wc_rules.modeling.pattern import GraphContainer, Pattern
from wc_rules.modeling.model import AggregateModel

gLynRecruitedDimer = recruit_to_ligand(2,recruit_to_receptor(build_receptor(['alpha','beta']), build_Lyn()))
gReceptorBeta = build_receptor(['alpha','beta'])
gReceptorGamma = build_receptor(['alpha','gamma'])
gReceptorSykLinker = recruit_to_receptor(build_receptor(['alpha','gamma']), build_Syk(['tsh2','linker']))

class LynKinaseModel(TransPhosphorylationModel):

    def __init__(self,target,target_receptor_graph,beta_state):
        g = GraphContainer.compose_from(
            gLynRecruitedDimer.duplicate().set_attr('beta_1','ph',beta_state),
            target_receptor_graph.add_suffix('2'),
        ).add_edge('fc_2','bond','alpha_2')
        super().__init__(target,f'{target}_2',g)
        self.verify(self.defaults)

lynstates = [('constitutive',False),('active',False)]
targetpairs = [('beta',gReceptorBeta),('gamma',gReceptorGamma),('linker',gReceptorSykLinker)]

models = []

constitutive_model = AggregateModel(
    name = 'constitutive',
    models = [LynKinaseModel(target,g,beta_state=False) for target,g in targetpairs]
)

active_model = AggregateModel(
    name = 'active',
    models = [LynKinaseModel(target,g,beta_state=True) for target,g in targetpairs]
)

model = AggregateModel(
    name = 'lyn_kinase',
    models = [constitutive_model, active_model]
)


data = {
    'constitutive':{
        'beta':     {'phosphorylation_rate': 30},
        'gamma':     {'phosphorylation_rate': 1},
        'linker':     {'phosphorylation_rate': 30}
    },
    'active': {
        'beta':     {'phosphorylation_rate': 100},
        'gamma':     {'phosphorylation_rate': 3},
        'linker':     {'phosphorylation_rate': 100}
    }
}
model.verify(data)
model.defaults = data