def SSAnetwork(fn,y0in,param,adjacency):
"""
This is the network-level SSA model, with coupling. Call with:
SSAcoupled,state_names,param_names = SSAmodelC(ODEmodel(),y0in,param)
To uncouple the model, set adjacency matrix to zeros
"""
#Converts concentration to population
y0in_ssa = (vol*y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names=[]
if randomy0==False:
y0in_pop = []
#coupling section===========================
for indx in range(cellcount):
index = '_'+str(indx)+'_0'
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [fn.inputSX(cs.DAE_X)[i].getDescription()+index
for i in xrange(EqCount)]
state_names = state_names + [fn.inputSX(cs.DAE_X)[i].getDescription()+index
for i in xrange(EqCount)]
if randomy0==False:
y0in_pop = np.append(y0in_pop, y0in_ssa)
if randomy0 == True:
#random initial locations
y0in_pop = 1*np.ones(EqCount*cellcount)
for i in range(len(y0in_pop)):
y0in_pop[i] = vol*4*random.random()
#===========================================
param_array = [fn.inputSX(cs.DAE_P)[i].getDescription()
for i in xrange(ParamCount)]
param_names = [fn.inputSX(cs.DAE_P)[i].getDescription()
for i in xrange(ParamCount)]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array,param_array,y0in_pop,param,SSAmodel,vol)
#coupling section
for indx in range(cellcount):
index = '_'+str(indx)+'_0'
#Coupled terms - - -------------------------------------------------
#loops for all cells accumulating their input
avg = '0'
mcount = 0
for fromcell in range(cellcount):
if adjacency[fromcell,indx]!= 0:
#The Coupling Part
mcount = mcount+1
avg = avg+'+M_'+str(fromcell)+'_0'
weight = 1.0/mcount
#FIXES PARAMETERS FROM DETERMINISTIC TO STOCHASTIC VALUES
if (str(SSA_builder.pvaldict['vs0']) ==
SSA_builder.SSAmodel.listOfParameters['vs0'].expression):
SSA_builder.SSAmodel.setParameter('vs0',
SSA_builder.SSAmodel.listOfParameters['vs0'].expression+'*('+str(SSA_builder.vol)+')')
if (str(SSA_builder.pvaldict['k1']) ==
SSA_builder.SSAmodel.listOfParameters['k1'].expression):
SSA_builder.SSAmodel.setParameter('k1',
SSA_builder.SSAmodel.listOfParameters['k1'].expression+'*('+str(SSA_builder.vol)+')')
if (str(SSA_builder.pvaldict['alocal']) ==
SSA_builder.SSAmodel.listOfParameters['alocal'].expression):
SSA_builder.SSAmodel.setParameter('alocal',
SSA_builder.SSAmodel.listOfParameters['alocal'].expression+'*('+str(SSA_builder.vol)+')')
#####
#Adds reaction for coupling
rxn=stk.Reaction(name='Cell'+str(indx)+'_Reaction0',
reactants={},
products={'M_'+str(indx)+'_0':1},
propensity_function=('std::max(0.0,(vs0+alocal*(('+avg+')*'
+str(weight)+'-M_'+str(indx)+'_0)))'+
'*pow(k1,n)/(pow(k1,n)+pow(Pn_'+str(indx)+'_0,n))'),annotation='')#
SSAmodel.addReaction(rxn)
#-------------------------------------------------------------------
# REACTIONS
SSA_builder.SSA_MM('Cell'+str(indx)+'_Reaction1','vm',
km=['km'],Rct=['M'+index])
SSA_builder.SSA_MA_tln('Cell'+str(indx)+'_Reaction2', 'Pc'+index,
'ks','M'+index)
SSA_builder.SSA_MM('Cell'+str(indx)+'_Reaction3','vd',
km=['kd'],Rct=['Pc'+index])
#The complexing four:
SSA_builder.SSA_MA_cytonuc('Cell'+str(indx)+'_Reaction4','Pc'+index,
'Pn'+index,'k1_','k2_')
return SSAmodel,state_names,param_names
def FullModel(Stoch=False):
# Variable Assignments
X = cs.ssym("X")
Y = cs.ssym("Y")
sys = cs.vertcat([X, Y]) # vector version of y
# Parameter Assignments
P = cs.ssym("P")
kt = cs.ssym("kt")
kd = cs.ssym("kd")
a0 = cs.ssym("a0")
a1 = cs.ssym("a1")
a2 = cs.ssym("a2")
kdx = cs.ssym("kdx")
paramset = cs.vertcat([P, kt, kd, a0, a1, a2, kdx])
# Time
t = cs.ssym("t")
ode = [[]] * EqCount
ode[0] = 1 / (1 + Y**P) - kdx * X
ode[1] = kt * X - kd * Y - Y / (a0 + a1 * Y + a2 * Y**2)
ode = cs.vertcat(ode)
fn = cs.SXFunction(cs.daeIn(t=t, x=sys, p=paramset), cs.daeOut(ode=ode))
fn.setOption("name", "2state")
#==================================================================
# Stochastic Model Portion
#==================================================================
if Stoch == True:
print 'Now converting model to StochKit XML format...'
#Converts concentration to population
y0in_pop = (vol * y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects
species_array = [
fn.inputSX(cs.DAE_X)[i].getDescription() for i in xrange(EqCount)
]
param_array = [
fn.inputSX(cs.DAE_P)[i].getDescription()
for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop,
param, SSAmodel, vol)
# REACTIONS
SSA_builder.SSA_tyson_x('x prod nonlinear term', 'X', 'Y', 'P')
SSA_builder.SSA_MA_deg('x degradation', 'X', 'kdx')
SSA_builder.SSA_MA_tln('y creation', 'Y', 'kt', 'X')
SSA_builder.SSA_MA_deg('y degradation, linear', 'Y', 'kd')
SSA_builder.SSA_tyson_y('y nonlinear term', 'Y', 'a0', 'a1', 'a2')
# END REACTIONS
state_names = [
fn.inputSX(cs.DAE_X)[i].getDescription() for i in xrange(EqCount)
]
param_names = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
return fn, SSAmodel, state_names, param_names
def SSAmodel(fn,y0in,param):
"""
This is the network-level SSA model, with coupling. Call with:
SSAcoupled,state_names,param_names = SSAmodelC(ODEmodel(),y0in,param)
By default, there is no coupling in this model.
"""
#Converts concentration to population
y0in_pop = (vol*y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = [fn.inputExpr(cs.DAE_X)[i].getName()
for i in xrange(EqCount)]
param_array = [fn.inputExpr(cs.DAE_P)[i].getName()
for i in xrange(ParamCount)]
state_names = [fn.inputExpr(cs.DAE_X)[i].getName()
for i in xrange(EqCount)]
param_names = [fn.inputExpr(cs.DAE_P)[i].getName()
for i in xrange(ParamCount)]
#creates SSAmodel class object
SSAmodel = stk.StochKitModel(name=modelversion)
SSA_builder = mb.SSA_builder(species_array,param_array,y0in_pop,param,SSAmodel,vol)
#FIXES PARAMETERS FROM DETERMINISTIC TO STOCHASTIC VALUES
if (str(SSA_builder.pvaldict['vs0']) ==
SSA_builder.SSAmodel.listOfParameters['vs0'].expression):
SSA_builder.SSAmodel.setParameter('vs0',
SSA_builder.SSAmodel.listOfParameters['vs0'].expression+'*('+str(SSA_builder.vol)+')')
if (str(SSA_builder.pvaldict['k1']) ==
SSA_builder.SSAmodel.listOfParameters['k1'].expression):
SSA_builder.SSAmodel.setParameter('k1',
SSA_builder.SSAmodel.listOfParameters['k1'].expression+'*('+str(SSA_builder.vol)+')')
if (str(SSA_builder.pvaldict['alocal']) ==
SSA_builder.SSAmodel.listOfParameters['alocal'].expression):
SSA_builder.SSAmodel.setParameter('alocal',
SSA_builder.SSAmodel.listOfParameters['alocal'].expression+'*('+str(SSA_builder.vol)+')')
# REACTIONS
rxn0=stk.Reaction(name='Reaction0',
reactants={},
products={'M':1},
propensity_function=(
'vs0*pow(k1,n)/(pow(k1,n)+pow(Pn,n))'),annotation='')
SSAmodel.addReaction(rxn0)
SSA_builder.SSA_MM('Reaction1','vm',
km=['km'],Rct=['M'])
SSA_builder.SSA_MA_tln('Reaction2', 'Pc',
'ks','M')
SSA_builder.SSA_MM('Reaction3','vd',
km=['kd'],Rct=['Pc'])
SSA_builder.SSA_MA_cytonuc('Reaction4','Pc',
'Pn','k1_','k2_')
return SSAmodel,state_names,param_names
def SSAmodelC(fn, y0in, param):
"""
This is the network-level SSA model, with coupling. Call with:
SSAcoupled,state_names,param_names = SSAmodelC(ODEmodel(),y0in,param)
To uncouple the model, set parameter 35 (last parameter) to 0, and also
set VIP production to 0.
"""
#Converts concentration to population
y0in_ssa = (vol * y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names = []
if randomy0 == False:
y0in_pop = []
#coupling section===========================
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
state_names = state_names + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
if randomy0 == False:
y0in_pop = np.append(y0in_pop, y0in_ssa)
if randomy0 == True:
#random initial locations
y0in_pop = 1 * np.ones(EqCount * xlen * ylen)
for i in range(len(y0in_pop)):
y0in_pop[i] = vol * 1 * random.random()
#===========================================
param_array = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
param_names = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop, param,
SSAmodel, vol)
#coupling section
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
# REACTIONS
#per mRNA
SSA_builder.SSA_PR16f('per mRNA activation' + index, 'p' + index,
'CREB' + index, 'C1P' + index, 'C2P' + index,
'vtpr', 'vtpp', 'knpr')
SSA_builder.SSA_MM('per mRNA degradation' + index,
'vdp',
km=['kdp'],
Rct=['p' + index])
#cry1 mRNA
SSA_builder.SSA_MM('c1 mRNA repression' + index,
'vtc1r',
km=['kncr'],
Prod=['c1' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('c1 mRNA degradation' + index,
'vdc1',
km=['kdc'],
Rct=['c1' + index])
#cry2 mRNA
SSA_builder.SSA_MM('c2 mRNA repression' + index,
'vtc2r',
km=['kncr'],
Prod=['c2' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('c2 mRNA degradation' + index,
'vdc2',
km=['kdc'],
Rct=['c2' + index])
#vip mRNA
SSA_builder.SSA_MM('vip mRNA repression' + index,
'vtvr',
km=['knvr'],
Prod=['vip' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('vip mRNA degradation' + index,
'vdv',
km=['kdv'],
Rct=['vip' + index])
#CRY1, CRY2, PER, VIP creation and degradation
SSA_builder.SSA_MA_tln('PER translation' + index, 'P' + index,
'ktlnp', 'p' + index)
SSA_builder.SSA_MA_tln('CRY1 translation' + index, 'C1' + index,
'ktlnc', 'c1' + index)
SSA_builder.SSA_MA_tln('CRY2 translation' + index, 'C2' + index,
'ktlnc', 'c2' + index)
SSA_builder.SSA_MA_tln('VIP translation' + index, 'eVIP' + index,
'ktlnv', 'vip' + index)
SSA_builder.SSA_MM('PER degradation' + index,
'vdP',
km=['kdP'],
Rct=['P' + index])
SSA_builder.SSA_MM('C1 degradation' + index,
'vdC1',
km=['kdC'],
Rct=['C1' + index])
SSA_builder.SSA_MM('C2 degradation' + index,
'vdC2',
km=['kdC'],
Rct=['C2' + index])
SSA_builder.SSA_MA_deg('eVIP degradation' + index, 'eVIP' + index,
'kdVIP')
#CRY1 CRY2 complexing
SSA_builder.SSA_MA_complex('CRY1-P complex' + index, 'C1' + index,
'P' + index, 'C1P' + index, 'vaCP',
'vdCP')
SSA_builder.SSA_MA_complex('CRY2-P complex' + index, 'C2' + index,
'P' + index, 'C2P' + index, 'vaCP',
'vdCP')
SSA_builder.SSA_MM('C1P degradation' + index,
'vdC1n',
km=['kdCn'],
Rct=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('C2P degradation' + index,
'vdC2n',
km=['kdCn'],
Rct=['C2P' + index, 'C1P' + index])
#VIP/CREB Pathway
SSA_builder.SSA_MM('CREB formation' + index,
'vgpka',
km=['kgpka'],
Prod=['CREB' + index],
Act=['eVIP' + index])
SSA_builder.SSA_MM('CREB degradation' + index,
'vdCREB',
km=['kdCREB'],
Rct=['CREB' + index])
SSA_builder.SSA_MA_meanfield('eVIP', indx, xlen, indy, ylen,
'kcouple')
return SSAmodel, state_names, param_names
def SSAcell(fn, y0in, param):
"""
This is the single cell-level SSA model. Call with:
SSAuncoupled,state_names,param_names = SSAmodelU(ODEmodel(),y0in,param)
"""
#Converts concentration to population
y0in_pop = (vol * y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = [
fn.inputSX(cs.DAE_X)[i].getDescription() for i in xrange(EqCount)
]
param_array = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#SSAmodel.units='concentration'
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop, param,
SSAmodel, vol)
# REACTIONS
#per mRNA
SSA_builder.SSA_PR16f('per mRNA activation', 'p', 'CREB', 'C1P', 'C2P',
'vtpr', 'vtpp', 'knpr')
SSA_builder.SSA_MM('per mRNA degradation', 'vdp', km=['kdp'], Rct=['p'])
#cry1 mRNA
SSA_builder.SSA_MM('c1 mRNA repression',
'vtc1r',
km=['kncr'],
Prod=['c1'],
Rep=['C1P', 'C2P'])
SSA_builder.SSA_MM('c1 mRNA degradation', 'vdc1', km=['kdc'], Rct=['c1'])
#cry2 mRNA
SSA_builder.SSA_MM('c2 mRNA repression',
'vtc2r',
km=['kncr'],
Prod=['c2'],
Rep=['C1P', 'C2P'])
SSA_builder.SSA_MM('c2 mRNA degradation', 'vdc2', km=['kdc'], Rct=['c2'])
#vip mRNA
SSA_builder.SSA_MM('vip mRNA repression',
'vtvr',
km=['knvr'],
Prod=['vip'],
Rep=['C1P', 'C2P'])
SSA_builder.SSA_MM('vip mRNA degradation', 'vdv', km=['kdv'], Rct=['vip'])
#CRY1, CRY2, PER, VIP creation and degradation
SSA_builder.SSA_MA_tln('PER translation', 'P', 'ktlnp', 'p')
SSA_builder.SSA_MA_tln('CRY1 translation', 'C1', 'ktlnc', 'c1')
SSA_builder.SSA_MA_tln('CRY2 translation', 'C2', 'ktlnc', 'c2')
SSA_builder.SSA_MA_tln('VIP translation', 'eVIP', 'ktlnv', 'vip')
SSA_builder.SSA_MM('PER degradation', 'vdP', km=['kdP'], Rct=['P'])
SSA_builder.SSA_MM('C1 degradation', 'vdC1', km=['kdC'], Rct=['C1'])
SSA_builder.SSA_MM('C2 degradation', 'vdC2', km=['kdC'], Rct=['C2'])
SSA_builder.SSA_MA_deg('eVIP degradation', 'eVIP', 'kdVIP')
#CRY1 CRY2 complexing
SSA_builder.SSA_MA_complex('CRY1-P complex', 'C1', 'P', 'C1P', 'vaCP',
'vdCP')
SSA_builder.SSA_MA_complex('CRY2-P complex', 'C2', 'P', 'C2P', 'vaCP',
'vdCP')
SSA_builder.SSA_MM('C1P degradation',
'vdC1n',
km=['kdCn'],
Rct=['C1P', 'C2P'])
SSA_builder.SSA_MM('C2P degradation',
'vdC2n',
km=['kdCn'],
Rct=['C2P', 'C1P'])
#VIP/CREB Pathway
SSA_builder.SSA_MM('CREB formation',
'vgpka',
km=['kgpka'],
Prod=['CREB'],
Act=['eVIP'])
SSA_builder.SSA_MM('CREB degradation',
'vdCREB',
km=['kdCREB'],
Rct=['CREB'])
state_names = [
fn.inputSX(cs.DAE_X)[i].getDescription() for i in xrange(EqCount)
]
param_names = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
return SSAmodel, state_names, param_names
def ssa_resync(fn, y0in_desync, param, cellcount, adjacency):
"""
This is the network-level SSA model, with coupling. Call with:
SSAcoupled,state_names,param_names = SSAmodelC(ODEmodel(),y0in,param)
To uncouple the model, set adjacency matrix to zeros
"""
#Converts concentration to population
y0in_ssa = (vol * y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names = []
y0in_pop = y0in_desync
#coupling section===========================
for indx in range(cellcount):
index = '_' + str(indx) + '_0'
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [
fn.inputExpr(cs.DAE_X)[i].getName() + index
for i in xrange(EqCount)
]
state_names = state_names + [
fn.inputExpr(cs.DAE_X)[i].getName() + index
for i in xrange(EqCount)
]
if randomy0 == False:
y0in_pop = np.append(y0in_pop, y0in_ssa)
if randomy0 == True:
#random initial locations
y0in_pop = 1 * np.ones(EqCount * cellcount)
for i in range(len(y0in_pop)):
y0in_pop[i] = vol * 1 * random.random()
#===========================================
param_array = [
fn.inputExpr(cs.DAE_P)[i].getName() for i in xrange(ParamCount)
]
param_names = [
fn.inputExpr(cs.DAE_P)[i].getName() for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop, param,
SSAmodel, vol)
#coupling section
for indx in range(cellcount):
index = '_' + str(indx) + '_0'
# REACTIONS
#per mRNA
SSA_builder.SSA_PR16f('per mRNA activation' + index, 'p' + index,
'CREB' + index, 'C1P' + index, 'C2P' + index,
'vtpr', 'vtpp', 'knpr')
SSA_builder.SSA_MM('per mRNA degradation' + index,
'vdp',
km=['kdp'],
Rct=['p' + index])
#cry1 mRNA
SSA_builder.SSA_MM('c1 mRNA repression' + index,
'vtc1r',
km=['kncr'],
Prod=['c1' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('c1 mRNA degradation' + index,
'vdc1',
km=['kdc'],
Rct=['c1' + index])
#cry2 mRNA
SSA_builder.SSA_MM('c2 mRNA repression' + index,
'vtc2r',
km=['kncr'],
Prod=['c2' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('c2 mRNA degradation' + index,
'vdc2',
km=['kdc'],
Rct=['c2' + index])
#vip mRNA
SSA_builder.SSA_MM('vip mRNA repression' + index,
'vtvr',
km=['knvr'],
Prod=['vip' + index],
Rep=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('vip mRNA degradation' + index,
'vdv',
km=['kdv'],
Rct=['vip' + index])
#CRY1, CRY2, PER, VIP creation and degradation
SSA_builder.SSA_MA_tln('PER translation' + index, 'P' + index, 'ktlnp',
'p' + index)
SSA_builder.SSA_MA_tln('CRY1 translation' + index, 'C1' + index,
'ktlnc', 'c1' + index)
SSA_builder.SSA_MA_tln('CRY2 translation' + index, 'C2' + index,
'ktlnc', 'c2' + index)
SSA_builder.SSA_MM('PER degradation' + index,
'vdP',
km=['kdP'],
Rct=['P' + index])
SSA_builder.SSA_MM('C1 degradation' + index,
'vdC1',
km=['kdC'],
Rct=['C1' + index])
SSA_builder.SSA_MM('C2 degradation' + index,
'vdC2',
km=['kdC'],
Rct=['C2' + index])
SSA_builder.SSA_MA_deg('eVIP degradation' + index, 'eVIP' + index,
'kdVIP')
#CRY1 CRY2 complexing
SSA_builder.SSA_MA_complex('CRY1-P complex' + index, 'C1' + index,
'P' + index, 'C1P' + index, 'vaCP', 'vdCP')
SSA_builder.SSA_MA_complex('CRY2-P complex' + index, 'C2' + index,
'P' + index, 'C2P' + index, 'vaCP', 'vdCP')
SSA_builder.SSA_MM('C1P degradation' + index,
'vdC1n',
km=['kdCn'],
Rct=['C1P' + index, 'C2P' + index])
SSA_builder.SSA_MM('C2P degradation' + index,
'vdC2n',
km=['kdCn'],
Rct=['C2P' + index, 'C1P' + index])
#VIP/CREB Pathway
SSA_builder.SSA_MM('CREB formation' + index,
'vgpka',
km=['kgpka'],
Prod=['CREB' + index],
Act=['eVIP' + index])
SSA_builder.SSA_MM('CREB degradation' + index,
'vdCREB',
km=['kdCREB'],
Rct=['CREB' + index])
for currentcell in range(cellcount):
for affectedcell in range(cellcount):
if adjacency[currentcell, affectedcell] != 0:
#The Coupling Part
rxn = stk.Reaction(
name='vip from ' + str(currentcell) + ' to ' +
str(affectedcell),
products={'eVIP_' + str(affectedcell) + '_0': 1},
propensity_function='vip_' + str(currentcell) + "_0*" +
'ktlnv' + '*' + str(adjacency[currentcell, affectedcell]),
annotation='')
SSAmodel.addReaction(rxn)
return SSAmodel, state_names, param_names
def SSAmodel(fn, y0):
#parameter initialization
xlen = xdim
ylen = ydim
#Converts concentration to population
y0in_ssa = (vol * y0).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names = []
y0in_pop = []
#coupling section===========================
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
state_names = state_names + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
y0in_pop = np.append(y0in_pop, y0in_ssa)
#===========================================
param_array = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
param_names = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop, param,
SSAmodel, vol)
#coupling section
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
# REACTIONS
SSA_builder.SSA_tyson_x('x prod nonlinear term' + index,
'X' + index, 'Y' + index, 'P')
SSA_builder.SSA_MA_deg('x degradation' + index, 'X' + index, 'kdx')
SSA_builder.SSA_MA_tln('y creation' + index, 'Y' + index, 'kt',
'X' + index)
SSA_builder.SSA_MA_deg('y degradation, linear' + index,
'Y' + index, 'kd')
SSA_builder.SSA_tyson_y('y nonlinear term' + index, 'Y' + index,
'a0', 'a1', 'a2')
return SSAmodel, state_names, param_names
def PulseModel(fn, y0pulse):
xlen = xdim
ylen = ydim
#Converts concentration to population
y0in_pop_pulse = y0pulse.astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names = []
parampulse = [2., 20., 1., 0.005, 0.05, 0.1, 0]
#[P, kt, kd, a0, a1, a2, kdx]
#coupling section===========================
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
state_names = state_names + [
fn.inputSX(cs.DAE_X)[i].getDescription() + index
for i in xrange(EqCount)
]
#===========================================
param_array = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
param_names = [
fn.inputSX(cs.DAE_P)[i].getDescription() for i in xrange(ParamCount)
]
#Names model
PulseModel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
Pulse_builder = mb.SSA_builder(species_array, param_array, y0in_pop_pulse,
parampulse, PulseModel, vol)
#coupling section
for indx in range(xlen):
for indy in range(ylen):
index = '_' + str(indx) + '_' + str(indy)
# REACTIONS
Pulse_builder.SSA_tyson_x('x prod nonlinear term' + index,
'X' + index, 'Y' + index, 'P')
Pulse_builder.SSA_MA_deg('x degradation' + index, 'X' + index,
'kdx')
Pulse_builder.SSA_MA_tln('y creation' + index, 'Y' + index, 'kt',
'X' + index)
Pulse_builder.SSA_MA_deg('y degradation, linear' + index,
'Y' + index, 'kd')
Pulse_builder.SSA_tyson_y('y nonlinear term' + index, 'Y' + index,
'a0', 'a1', 'a2')
return PulseModel
def ssa_resync(fn,
y0in_desync,
param,
cellcount,
adjacency,
couplingstr=couplingstr,
vol=40):
"""
This is the network-level SSA model, with coupling. Call with:
SSAcoupled,state_names,param_names = SSAmodelC(ODEmodel(),y0in,param)
To uncouple the model, set adjacency matrix to zeros
"""
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = []
state_names = []
#coupling section===========================
for indx in range(cellcount):
index = '_' + str(indx) + '_0'
#loops to include all species, normally this is the only line needed without index
species_array = species_array + [
fn.inputExpr(cs.DAE_X)[i].getName() + index
for i in xrange(EqCount)
]
state_names = state_names + [
fn.inputExpr(cs.DAE_X)[i].getName() + index
for i in xrange(EqCount)
]
y0in_pop = y0in_desync
#===========================================
param_array = [
fn.inputExpr(cs.DAE_P)[i].getName() for i in xrange(ParamCount)
]
param_names = [
fn.inputExpr(cs.DAE_P)[i].getName() for i in xrange(ParamCount)
]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array, param_array, y0in_pop, param,
SSAmodel, vol)
# now set up mean field(?) coupling for all uncoupled cells
#coupling section
for indx in range(cellcount):
index = '_' + str(indx) + '_0'
#Coupled terms - - -------------------------------------------------
#loops for all cells accumulating their input
avg = 'M' + index
mcount = 1
for fromcell in range(cellcount):
if adjacency[fromcell, indx] != 0:
#The Coupling Part
mcount = mcount + couplingstr
avg = avg + '+M_' + str(fromcell) + '_0*' + str(couplingstr)
if mcount == 1:
# then the cell is "uncoupled" so we'll put it to a mean field
for fromcell in range(cellcount):
mcount += couplingstr * 0.25 / cellcount
avg = avg + '+M_' + str(fromcell) + '_0*0.33*' + str(
couplingstr / cellcount)
weight = 1.0 / mcount
#FIXES PARAMETERS FROM DETERMINISTIC TO STOCHASTIC VALUES
if (str(SSA_builder.pvaldict['vs0']) ==
SSA_builder.SSAmodel.listOfParameters['vs0'].expression):
SSA_builder.SSAmodel.setParameter(
'vs0',
SSA_builder.SSAmodel.listOfParameters['vs0'].expression +
'*(' + str(SSA_builder.vol) + ')')
if (str(SSA_builder.pvaldict['k1']) ==
SSA_builder.SSAmodel.listOfParameters['k1'].expression):
SSA_builder.SSAmodel.setParameter(
'k1', SSA_builder.SSAmodel.listOfParameters['k1'].expression +
'*(' + str(SSA_builder.vol) + ')')
if (str(SSA_builder.pvaldict['alocal']) ==
SSA_builder.SSAmodel.listOfParameters['alocal'].expression):
SSA_builder.SSAmodel.setParameter(
'alocal',
SSA_builder.SSAmodel.listOfParameters['alocal'].expression +
'*(' + str(SSA_builder.vol) + ')')
#####
#Adds reaction
rxn = stk.Reaction(
name='Cell' + str(indx) + '_Reaction0',
reactants={},
products={'M_' + str(indx) + '_0': 1},
propensity_function=('std::max(0.0,(vs0+alocal*((' + avg + ')*' +
str(weight) + '-M_' + str(indx) + '_0)))' +
'*pow(k1,n)/(pow(k1,n)+pow(Pn_' + str(indx) +
'_0,n))'),
annotation='') #
SSAmodel.addReaction(rxn)
#-------------------------------------------------------------------
# REACTIONS
SSA_builder.SSA_MM('Cell' + str(indx) + '_Reaction1',
'vm',
km=['km'],
Rct=['M' + index])
SSA_builder.SSA_MA_tln('Cell' + str(indx) + '_Reaction2', 'Pc' + index,
'ks', 'M' + index)
SSA_builder.SSA_MM('Cell' + str(indx) + '_Reaction3',
'vd',
km=['kd'],
Rct=['Pc' + index])
#The complexing four:
SSA_builder.SSA_MA_cytonuc('Cell' + str(indx) + '_Reaction4',
'Pc' + index, 'Pn' + index, 'k1_', 'k2_')
print 'weak, mean-field coupling'
return SSAmodel, state_names, param_names
def StochModel():
#==================================================================
# Stochastic Model Setup
#==================================================================
print 'Now converting model to StochKit XML format...'
#Converts concentration to population for initial values
y0in_stoch = (vol*y0in).astype(int)
#collects state and parameter array to be converted to species and parameter objects,
#makes copies of the names so that they are on record
species_array = ['p', 'c1', 'c2', 'vip', 'P', 'C1', 'C2', 'eVIP', 'C1P', 'C2P', 'CREB']
param_array = ['vtpr' , 'vtc1r' , 'vtc2r' , 'knpr' , 'kncr' ,
'vdp' , 'vdc1' , 'vdc2' , 'kdp' , 'kdc' ,
'vdP' , 'kdP' , 'vdC1' , 'vdC2' , 'kdC' ,
'vdC1n' , 'vdC2n' , 'kdCn' , 'vaCP' , 'vdCP' , 'ktlnp',
'vtpp' , 'vtc1p' , 'vtc2p' , 'vtvp' , 'vtvr' ,
'knpp' , 'kncp' , 'knvp' , 'knvr' , 'vdv' ,
'kdv' , 'vdVIP' , 'kdVIP' , 'vgpcr' , 'kgpcr' ,
'vdCREB', 'kdCREB', 'ktlnv' , 'vdpka' , 'vgpka' ,
'kdpka' , 'kgpka' , 'kdc1' , 'kdc2' , 'ktlnc']
#duplicated for names later
state_names=species_array[:]
param_names=param_array[:]
#Names model
SSAmodel = stk.StochKitModel(name=modelversion)
#SSAmodel.units='concentration'
#creates SSAmodel class object
SSA_builder = mb.SSA_builder(species_array,param_array,y0in_stoch,param,SSAmodel,vol)
# REACTIONS
#per mRNA
SSA_builder.SSA_MM('per mRNA activation','vtpp',km=['knpp'],Prod=['p'],Act=['CREB'])
SSA_builder.SSA_MM('per mRNA repression','vtpr',km=['knpr'],Prod=['p'],Rep=['C1P','C2P'])
SSA_builder.SSA_MM('per mRNA degradation','vdp',km=['kdp'],Rct=['p'])
#cry1 mRNA
SSA_builder.SSA_MM('c1 mRNA activation','vtc1p',km=['kncp'],Prod=['c1'],Act=['CREB'])
SSA_builder.SSA_MM('c1 mRNA repression','vtc1r',km=['kncr'],Prod=['c1'],Rep=['C1P','C2P'])
SSA_builder.SSA_MM('c1 mRNA degradation','vdc1',km=['kdc'],Rct=['c1'])
#cry2 mRNA
SSA_builder.SSA_MM('c2 mRNA activation','vtc2p',km=['kncp'],Prod=['c2'],Act=['CREB'])
SSA_builder.SSA_MM('c2 mRNA repression','vtc2r',km=['kncr'],Prod=['c2'],Rep=['C1P','C2P'])
SSA_builder.SSA_MM('c2 mRNA degradation','vdc2',km=['kdc'],Rct=['c2'])
#vip mRNA
SSA_builder.SSA_MM('vip mRNA activation','vtvp',km=['knvp'],Prod=['vip'],Act=['CREB'])
SSA_builder.SSA_MM('vip mRNA repression','vtvr',km=['knvr'],Prod=['vip'],Rep=['C1P','C2P'])
SSA_builder.SSA_MM('vip mRNA degradation','vdv',km=['kdv'],Rct=['vip'])
#CRY1, CRY2, PER, VIP creation and degradation
SSA_builder.SSA_MA_tln('PER translation' ,'P' ,'ktlnp','p')
SSA_builder.SSA_MA_tln('CRY1 translation','C1' ,'ktlnc','c1')
SSA_builder.SSA_MA_tln('CRY2 translation','C2' ,'ktlnc','c2')
SSA_builder.SSA_MA_tln('VIP translation' ,'eVIP','ktlnv','vip')
SSA_builder.SSA_MM('PER degradation','vdP',km=['kdP'],Rct=['P'])
SSA_builder.SSA_MM('C1 degradation','vdC1',km=['kdC'],Rct=['C1'])
SSA_builder.SSA_MM('C2 degradation','vdC2',km=['kdC'],Rct=['C2'])
SSA_builder.SSA_MA_deg('eVIP degradation','eVIP','kdVIP')
#CRY1 CRY2 complexing
SSA_builder.SSA_MA_complex('CRY1-P complex','C1','P','C1P','vaCP','vdCP')
SSA_builder.SSA_MA_complex('CRY2-P complex','C2','P','C2P','vaCP','vdCP')
SSA_builder.SSA_MM('C1P degradation','vdC1n',km=['kdCn'],Rct=['C1P','C2P'])
SSA_builder.SSA_MM('C2P degradation','vdC2n',km=['kdCn'],Rct=['C2P','C1P'])
#VIP/CREB Pathway
SSA_builder.SSA_MM('CREB formation','vgpka',km=['kgpka'],Prod=['CREB'],Act=['eVIP'])
SSA_builder.SSA_MM('CREB degradation','vdCREB',km=['kdCREB'],Rct=['CREB'])
# END REACTIONS
#stringSSAmodel = SSAmodel.serialize()
#print stringSSAmodel
return SSAmodel,state_names,param_names
