def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="Trichloroethylene")
# Species
A = Species(name='TCE', initial_value=300)
B = Species(name='Epoxide', initial_value=120)
C = Species(name='Dichloracatate', initial_value=0)
D = Species(name='LossOfOneCL', initial_value=0)
E = Species(name='Glyoxylate', initial_value=0)
F = Species(name='Output', initial_value=0)
self.add_species([A, B, C, D, E, F])
# Parameters
K1 = Parameter(name='K1', expression=0.00045 * 0.000025)
K2 = Parameter(name='K2', expression=14)
K3 = Parameter(name='K3', expression=0.033)
K4 = Parameter(name='K4', expression=500 * 0.0001)
K5 = Parameter(name='K5', expression=500 * 0.0001)
self.add_parameter([K1, K2, K3, K4, K5])
# Reactions
J1 = Reaction(name="J1", reactants={A: 1}, products={B: 1}, rate=K2)
J2 = Reaction(name="J2", reactants={B: 1}, products={C: 1}, rate=K3)
J3 = Reaction(name="J3", reactants={C: 1}, products={D: 1}, rate=K4)
J4 = Reaction(name="J4", reactants={D: 1}, products={E: 1}, rate=K4)
J5 = Reaction(name="J5", reactants={E: 1}, products={F: 1}, rate=K5)
self.add_reaction([J1, J2, J3, J4, J5])
self.timespan(np.linspace(0, 10000, 100))
def __init__(self, parameter_values=None):
# Initialize the model.
Model.__init__(self, name="Toggle_Switch")
# Species
A = Species(name='A', initial_value=10.0)
B = Species(name='B', initial_value=10.0)
self.add_species([A, B])
# Parameters
alpha1 = Parameter(name='alpha1', expression=10.0)
alpha2 = Parameter(name='alpha2', expression=10.0)
beta = Parameter(name='beta', expression=2.0)
gamma = Parameter(name='gamma', expression=2.0)
mu = Parameter(name='mu', expression=1.0)
self.add_parameter([alpha1, alpha2, beta, gamma, mu])
# Reactions
cu = Reaction(name="r1",
reactants={},
products={A: 1},
rate=alpha1.value *
(1 + B.initial_value**float(beta.expression)))
cv = Reaction(
name="r2",
reactants={},
products={B: 1},
rate=alpha2.value(1 + A.initial_value**float(gamma.expression)))
du = Reaction(name="r3", reactants={A: 1}, products={}, rate=mu)
dv = Reaction(name="r4", reactants={B: 1}, products={}, rate=mu)
self.add_reaction([cu, cv, du, dv])
self.timespan(np.linspace(0, 250, 251))
def __init__(self, parameter_values=None):
Model.__init__(self, name="Simple_Hybrid_Model")
# Species
A = Species(name='A', initial_value=0)
B = Species(name='B', initial_value=0)
self.add_species([A, B])
# Parameters
k1 = Parameter(name='k1', expression=1)
k2 = Parameter(name='k2', expression=10)
self.add_parameter([k1, k2])
# Rate Rule
rate_rule = RateRule(B, "cos(t)")
self.add_rate_rule(rate_rule)
# Reactions
r1 = Reaction(name='r1',
reactants={A: 1},
products={},
propensity_function="k1*B")
r2 = Reaction(name='r2', reactants={}, products={B: 1}, rate=k2)
self.add_reaction([r1, r2])
self.timespan(numpy.linspace(0, 1, 11))
def __init__(self, parameter_values=None):
# First call the gillespy2.Model initializer.
Model.__init__(self, name="Dimerization")
# Define parameters for the rates of creation and dissociation.
k_c = Parameter(name='k_c', expression=0.005)
k_d = Parameter(name='k_d', expression=0.08)
self.add_parameter([k_c, k_d])
# Define variables for the molecular species representing M and D.
m = Species(name='monomer', initial_value=30)
d = Species(name='dimer', initial_value=0)
self.add_species([m, d])
# Define the reactions representing the process. In GillesPy2,
# the list of reactants and products for a Reaction object are each a
# Python dictionary in which the dictionary keys are Species objects
# and the values are stoichiometries of the species in the reaction.
r_creation = Reaction(name="r_creation",
rate=k_c,
reactants={m: 2},
products={d: 1})
r_dissociation = Reaction(name="r_dissociation",
rate=k_d,
reactants={d: 1},
products={m: 2})
self.add_reaction([r_creation, r_dissociation])
# Set the timespan for the simulation.
self.timespan(np.linspace(0, 100, 101))
def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="Michaelis_Menten")
# parameters
rate1 = Parameter(name='rate1', expression=0.0017)
rate2 = Parameter(name='rate2', expression=0.5)
rate3 = Parameter(name='rate3', expression=0.1)
self.add_parameter([rate1, rate2, rate3])
# Species
A = Species(name='A', initial_value=301)
B = Species(name='B', initial_value=120)
C = Species(name='C', initial_value=0)
D = Species(name='D', initial_value=0)
self.add_species([A, B, C, D])
# reactions
r1 = Reaction(name="r1", reactants={A: 1, B: 1}, products={C: 1}, rate=rate1)
r2 = Reaction(name="r2", reactants={C: 1}, products={A: 1, B: 1}, rate=rate2)
r3 = Reaction(name="r3", reactants={C: 1}, products={B: 1, D: 1}, rate=rate3)
self.add_reaction([r1, r2, r3])
self.timespan(np.linspace(0, 100, 101))
def __init__(self, parameter_values=None, init_v=1):
# initialize Model
Model.__init__(self, name="Simple_Hybrid_Model")
# Species
A = Species(name='A', initial_value=0)
V = Species(name='V', initial_value=init_v)
self.add_species([A, V])
# parameters
rate1 = Parameter(name='rate1', expression=20.0)
rate2 = Parameter(name='rate2', expression=10.0)
rate_rule1 = RateRule(V, "cos(t)")
self.add_parameter([rate1, rate2])
self.add_rate_rule(rate_rule1)
# reactions
r1 = Reaction(name="r1",
reactants={},
products={A: 1},
propensity_function="rate1 * V")
r2 = Reaction(name="r2", reactants={A: 1}, products={}, rate=rate2)
self.add_reaction([r1, r2])
self.timespan(np.linspace(0, 100, 1001))
def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="Schlogl")
# Species
s1 = Species(name='A', initial_value=300)
s2 = Species(name='B', initial_value=300)
s3 = Species(name='C', initial_value=300)
s4 = Species(name='X', initial_value=300)
self.add_species([s1, s2, s3, s4])
k1 = Parameter(name='k1', expression=1)
k2 = Parameter(name='k2', expression=1)
self.add_parameter([k1, k2])
j1 = Reaction(name="j1",
reactants={
s1: 1,
s4: 1
},
products={s4: 2.0},
rate=k1)
j2 = Reaction(name="j2",
reactants={
s2: 1,
s4: 1
},
products={s3: 1},
rate=k2)
self.add_reaction([j1, j2])
self.timespan(np.linspace(0, 100000, 100))
def __init__(self, parameter_values=None):
Model.__init__(self, name="tyson-2-state", volume=300.0)
# Species
X = Species(name='X', initial_value=int(0.65609071 * 300.0))
Y = Species(name='Y', initial_value=int(0.85088331 * 300.0))
self.add_species([X, Y])
P = Parameter(name='p', expression=2.0)
kt = Parameter(name='kt', expression=20.0)
kd = Parameter(name='kd', expression=1.0)
a0 = Parameter(name='a0', expression=0.005)
a1 = Parameter(name='a1', expression=0.05)
a2 = Parameter(name='a2', expression=0.1)
kdx = Parameter(name='kdx', expression=1.0)
self.add_parameter([P, kt, kd, a0, a1, a2, kdx])
# creation of X:
rxn1 = Reaction(
name='X production',
reactants={},
products={X: 1},
propensity_function='300 * 1.0 / (1.0 + (Y * Y / (300 * 300)))')
# degradadation of X:
rxn2 = Reaction(name='X degradation',
reactants={X: 1},
products={},
rate=kdx)
# creation of Y:
rxn3 = Reaction(name='Y production',
reactants={X: 1},
products={
X: 1,
Y: 1
},
rate=kt)
# degradation of Y:
rxn4 = Reaction(name='Y degradation',
reactants={Y: 1},
products={},
rate=kd)
# nonlinear Y term:
rxn5 = Reaction(name='Y nonlin',
reactants={Y: 1},
products={},
propensity_function=
'Y / a0 + a1 * (Y / 300) + a2 * Y * Y / (300 * 300)')
self.add_reaction([rxn1, rxn2, rxn3, rxn4, rxn5])
self.timespan(np.linspace(0, 100, 101))
def __init__(self, input1, input2, weight1, weight2, record_keeper, pNeg, pPos, time, k_array):
self.input1 = input1
self.input2 = input2
self.weight1 = weight1
self.weight2 = weight2
self.record_keeper = record_keeper
self.pNeg = pNeg
self.pPos = pPos
self.time = time
self.k_array = k_array
system_volume = 100
constant = 1
Model.__init__(self, name="Hidden_NN", volume=system_volume)
x1 = Species(name='input 1', initial_value=int(self.input1*system_volume))
x2 = Species(name='input 2', initial_value=int(self.input2*system_volume))
w1 = Species(name='weight 1', initial_value=int(self.weight1))
w2 = Species(name='weight 2', initial_value=int(self.weight2))
y = Species(name='output', initial_value=0)
xy = Species(name='record_keeper', initial_value=int(self.record_keeper))
wPlus = Species(name='positive weight error', initial_value=0)
wMinus = Species(name='negative weight error', initial_value=0)
wNeg = Species(name='weight annihilator', initial_value=0)
sF = Species(name="feed forward signal", initial_value=constant*system_volume)
f = Species(name="feed forward input", initial_value=0)
membrane = Species(name='cell membrane', initial_value=constant*system_volume)
pNeg = Species(name='hidden neg penalty', initial_value=int(self.pNeg))
pPos = Species(name='hidden pos penalty', initial_value=int(self.pPos))
self.add_species([x1, x2, w1, w2, y, xy, wPlus, wMinus, wNeg, sF, f, membrane, pNeg, pPos])
k1 = Parameter(name='k1', expression=self.k_array[0])
k2 = Parameter(name='k2', expression=self.k_array[1])
k3 = Parameter(name='k3', expression=self.k_array[2])
k4 = Parameter(name='k4', expression=self.k_array[3])
k5 = Parameter(name='k5', expression=self.k_array[4])
k6 = Parameter(name='k6', expression=self.k_array[5])
self.add_parameter([k1, k2, k3, k4, k5, k6])
rxn11 = Reaction(name='input 1 to output', reactants={x1: 1, w1: 1}, products={y: 1, xy: 1, w1: 1}, rate=k1)
rxn12 = Reaction(name='input 2 to output', reactants={x2: 1, w2: 1}, products={y: 1, xy: 1, w2: 1}, rate=k1)
rxn21 = Reaction(name='x1 annihilation', reactants={x1: 1, y: 1}, products={}, rate=k2)
rxn22 = Reaction(name='x2 annihilation', reactants={x2: 1, y: 1}, products={}, rate=k2)
rxn3 = Reaction(name='positive weight adjustment', reactants={wPlus: 1, xy: 1}, products={w1: 1, w2: 1, xy: 1}, rate=k3)
rxn41 = Reaction(name='negative weight adjustment 1', reactants={wMinus: 1, xy: 1}, products={wNeg: 1, xy: 1}, rate=k4)
rxn42 = Reaction(name='negative weight adjustment 2', reactants={wNeg: 1, w1: 1}, products={}, rate=k4)
rxn43 = Reaction(name='negative weight adjustment 3', reactants={wNeg: 1, w2: 1}, products={}, rate=k4)
rxn5 = Reaction(name='output to input', reactants={y: 1, sF: 1}, products={f: 1, sF: 1}, rate=k5)
rxn61 = Reaction(name='create pos weight adjust', reactants={pPos: 1, membrane: 1}, products={wPlus: 1, membrane: 1}, rate=k6)
rxn62 = Reaction(name='create neg weight adjust', reactants={pNeg: 1, membrane: 1}, products={wMinus: 1, membrane: 1}, rate=k6)
self.add_reaction([rxn11, rxn12, rxn21, rxn22, rxn3, rxn41, rxn42, rxn43, rxn5, rxn61, rxn62])
self.timespan(self.time)
def __init__(self, parameter_values=None):
# Initialize the model.
Model.__init__(self, name="Example")
# Species
S = Species(name='Sp', initial_value=100)
self.add_species([S])
# Parameters
k1 = Parameter(name='k1', expression=3.0)
self.add_parameter([k1])
# Reactions
rxn1 = Reaction(name='S degradation', reactants={S: 1}, products={}, rate=k1)
self.add_reaction([rxn1])
self.timespan(np.linspace(0, 20, 101))
def __init__(self, parameter_values=None):
# Initialize the model.
Model.__init__(self, name="Toggle_Switch")
# Species
A = Species(name='A', initial_value=10)
B = Species(name='B', initial_value=10)
self.add_species([A, B])
# Parameters
alpha1 = Parameter(name='alpha1', expression=10)
alpha2 = Parameter(name='alpha2', expression=10)
beta = Parameter(name='beta', expression=2)
gamma = Parameter(name='gamma', expression=2)
mu = Parameter(name='mu', expression=1)
self.add_parameter([alpha1, alpha2, beta, gamma, mu])
# Reactions
self.add_reaction(
Reaction(name="cu",
reactants={},
products={'A': 1},
propensity_function="alpha1/(1+pow(B, beta))"))
self.add_reaction(
Reaction(name="cv",
reactants={},
products={'B': 1},
propensity_function="alpha2/(1+pow(A, gamma))"))
self.add_reaction(
Reaction(name="du",
reactants={'A': 1},
products={},
rate=self.listOfParameters["mu"]))
self.add_reaction(
Reaction(name="dv",
reactants={'B': 1},
products={},
rate=self.listOfParameters["mu"]))
self.timespan(np.linspace(0, 250, 251))
def __init__(self, parameter_values=None):
# Superclass initialization
Model.__init__(self, name="Oregonator")
# Species
F = Species(name="F", initial_value=2)
A = Species(name="A", initial_value=250)
B = Species(name="B", initial_value=500)
C = Species(name="C", initial_value=1000)
P = Species(name="P", initial_value=0)
self.add_species([F, A, B, C, P])
# Parameters (rates)
k1 = Parameter(name="k1", expression=2.0)
k2 = Parameter(name="k2", expression=0.1)
k3 = Parameter(name="k3", expression=104)
k4 = Parameter(name="k4", expression=4e-7)
k5 = Parameter(name="k5", expression=26.0)
self.add_parameter([k1, k2, k3, k4, k5])
# Reactions
reaction1 = Reaction(name="reaction1",
reactants={
B: 1,
F: 1
},
products={
A: 1,
F: 1
},
rate=k1)
reaction2 = Reaction(name="reaction2",
reactants={
A: 1,
B: 1
},
products={P: 1},
rate=k2)
reaction3 = Reaction(name="reaction3",
reactants={
A: 1,
F: 1
},
products={
A: 2,
C: 1,
F: 1
},
rate=k3)
reaction4 = Reaction(name="reaction4",
reactants={A: 2},
products={P: 1},
rate=k4)
reaction5 = Reaction(name="reaction5",
reactants={
C: 1,
F: 1
},
products={
B: 1,
F: 1
},
rate=k5)
self.add_reaction(
[reaction1, reaction2, reaction3, reaction4, reaction5])
# Set timespan of model
self.timespan(np.linspace(0, 5, 501))
def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="LacOperon")
# Species
s1 = Species(name='PLac', initial_value=300)
s2 = Species(name='RNAP', initial_value=120)
s3 = Species(name='PLacRNAP', initial_value=0)
s4 = Species(name='TrLacZ1', initial_value=0)
s5 = Species(name='TrLacZ2', initial_value=0)
s6 = Species(name='TrLacY1', initial_value=0)
s7 = Species(name='TrLacY2', initial_value=0)
s8 = Species(name='RBSLacY', initial_value=300)
s9 = Species(name='RBSLacZ', initial_value=120)
s10 = Species(name='Ribosome', initial_value=0)
s11 = Species(name='RbsRibosomeLacY', initial_value=0)
s12 = Species(name='RbsRibosomeLacZ', initial_value=0)
s13 = Species(name='TrRbsLacZ', initial_value=0)
s14 = Species(name='TrRbsLacY', initial_value=0)
s15 = Species(name='LacZ', initial_value=0)
s16 = Species(name='LacY', initial_value=0)
s17 = Species(name='dgrLacZ', initial_value=0)
s18 = Species(name='dgrLacY', initial_value=0)
s19 = Species(name='dgrRbsLacZ', initial_value=0)
s20 = Species(name='dgrRbsLacY', initial_value=0)
s21 = Species(name='lactose', initial_value=0)
s22 = Species(name='LacZlactose', initial_value=0)
s23 = Species(name='product', initial_value=0)
self.add_species(
[s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17, s18, s19, s20, s21, s22,
s23])
# Parameters
k1 = Parameter(name='k1', expression=0.17)
k2 = Parameter(name='k2', expression=10)
k3 = Parameter(name='k3', expression=1)
k4 = Parameter(name='k4', expression=1)
k5 = Parameter(name='k5', expression=0.015)
k6 = Parameter(name='k6', expression=1)
k7 = Parameter(name='k7', expression=0.36)
k8 = Parameter(name='k8', expression=0.17)
k9 = Parameter(name='k9', expression=0.17)
k10 = Parameter(name='k10', expression=0.45)
k11 = Parameter(name='k11', expression=0.45)
k12 = Parameter(name='k12', expression=0.4)
k13 = Parameter(name='k13', expression=0.4)
k14 = Parameter(name='k14', expression=0.015)
k15 = Parameter(name='k15', expression=0.036)
k16 = Parameter(name='k16', expression=6.42e-5)
k17 = Parameter(name='k17', expression=6.42e-5)
k18 = Parameter(name='k18', expression=0.3)
k19 = Parameter(name='k19', expression=0.3)
k20 = Parameter(name='k20', expression=9.52e-5)
k21 = Parameter(name='k21', expression=431)
k22 = Parameter(name='22', expression=14)
self.add_parameter(
[k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12, k13, k14, k15, k16, k17, k18, k19, k20, k21, k22])
# Reactions
j1 = Reaction(name="j1", reactants={s1: 1, s2: 1}, products={s3: 2.0}, rate=k1)
j2 = Reaction(name="j2", reactants={s3: 1}, products={s1: 1, s2: 1}, rate=k2)
j3 = Reaction(name="j3", reactants={s3: 1}, products={s4: 1}, rate=k3)
j4 = Reaction(name="j4", reactants={s4: 1}, products={s9: 1, s1: 1, s5: 1}, rate=k4)
j5 = Reaction(name="j5", reactants={s5: 1}, products={s6: 1}, rate=k5)
j6 = Reaction(name="j6", reactants={s6: 1}, products={s8: 1, s7: 1}, rate=k6)
j7 = Reaction(name="j7", reactants={s7: 1}, products={s2: 1}, rate=k7)
j8 = Reaction(name="j8", reactants={s10: 1, s9: 1}, products={s12: 1}, rate=k8)
j9 = Reaction(name="j9", reactants={s10: 1, s8: 1}, products={s11: 1}, rate=k9)
j10 = Reaction(name="j10", reactants={s12: 1}, products={s10: 1, s9: 1}, rate=k10)
j11 = Reaction(name="j11", reactants={s11: 1}, products={s10: 1, s8: 1}, rate=k11)
j12 = Reaction(name="j12", reactants={s12: 1}, products={s13: 1, s9: 1}, rate=k12)
j13 = Reaction(name="j13", reactants={s11: 1}, products={s14: 1, s8: 1}, rate=k13)
j14 = Reaction(name="j14", reactants={s13: 1}, products={s15: 1}, rate=k14)
j15 = Reaction(name="j15", reactants={s14: 1}, products={s16: 1}, rate=k15)
j16 = Reaction(name="j16", reactants={s15: 1}, products={s17: 1}, rate=k16)
j17 = Reaction(name="j17", reactants={s16: 1}, products={s18: 1}, rate=k17)
j18 = Reaction(name="j18", reactants={s9: 1}, products={s19: 1}, rate=k18)
j19 = Reaction(name="j19", reactants={s8: 1}, products={s20: 1}, rate=k19)
j20 = Reaction(name="j20", reactants={s15: 1, s21: 1}, products={s22: 1}, rate=k20)
j21 = Reaction(name="j21", reactants={s22: 1}, products={s15: 1, s23: 1}, rate=k21)
j22 = Reaction(name="j22", reactants={s16: 1}, products={s21: 1, s16: 1}, rate=k22)
self.add_reaction(
[j1, j2, j3, j4, j5, j6, j7, j8, j9, j10, j11, j12, j13, j14, j15, j16, j17, j18, j19, j20, j21, j22])
self.timespan(np.linspace(0, 100, 10))
def __init__(self, parameter_values=None):
Model.__init__(self)
def __init__(self, parameter_values=None):
Model.__init__(self, name="Vilar_Oscillator")
self.volume = 1
# Parameters
self.add_parameter(Parameter(name="alpha_a", expression=50))
self.add_parameter(Parameter(name="alpha_a_prime", expression=500))
self.add_parameter(Parameter(name="alpha_r", expression=0.01))
self.add_parameter(Parameter(name="alpha_r_prime", expression=50))
self.add_parameter(Parameter(name="beta_a", expression=50))
self.add_parameter(Parameter(name="beta_r", expression=5))
self.add_parameter(Parameter(name="delta_ma", expression=10))
self.add_parameter(Parameter(name="delta_mr", expression=0.5))
self.add_parameter(Parameter(name="delta_a", expression=1))
self.add_parameter(Parameter(name="delta_r", expression=0.2))
self.add_parameter(Parameter(name="gamma_a", expression=1))
self.add_parameter(Parameter(name="gamma_r", expression=1))
self.add_parameter(Parameter(name="gamma_c", expression=2))
self.add_parameter(Parameter(name="theta_a", expression=50))
self.add_parameter(Parameter(name="theta_r", expression=100))
# Species
self.add_species(Species(name="Da", initial_value=1, mode="discrete"))
self.add_species(
Species(name="Da_prime", initial_value=0, mode="discrete"))
self.add_species(Species(name="Ma", initial_value=0, mode="discrete"))
self.add_species(Species(name="Dr", initial_value=1, mode="discrete"))
self.add_species(
Species(name="Dr_prime", initial_value=0, mode="discrete"))
self.add_species(Species(name="Mr", initial_value=0, mode="discrete"))
self.add_species(Species(name="C", initial_value=10, mode="discrete"))
self.add_species(Species(name="A", initial_value=10, mode="discrete"))
self.add_species(Species(name="R", initial_value=10, mode="discrete"))
# Reactions
self.add_reaction(
Reaction(name="r1",
reactants={'Da_prime': 1},
products={'Da': 1},
rate=self.listOfParameters["theta_a"]))
self.add_reaction(
Reaction(name="r2",
reactants={
'Da': 1,
'A': 1
},
products={'Da_prime': 1},
rate=self.listOfParameters["gamma_a"]))
self.add_reaction(
Reaction(name="r3",
reactants={'Dr_prime': 1},
products={'Dr': 1},
rate=self.listOfParameters["theta_r"]))
self.add_reaction(
Reaction(name="r4",
reactants={
'Dr': 1,
'A': 1
},
products={'Dr_prime': 1},
rate=self.listOfParameters["gamma_r"]))
self.add_reaction(
Reaction(name="r5",
reactants={'Da_prime': 1},
products={
'Da_prime': 1,
'Ma': 1
},
rate=self.listOfParameters["alpha_a_prime"]))
self.add_reaction(
Reaction(name="r6",
reactants={'Da': 1},
products={
'Da': 1,
'Ma': 1
},
rate=self.listOfParameters["alpha_a"]))
self.add_reaction(
Reaction(name="r7",
reactants={'Ma': 1},
products={},
rate=self.listOfParameters["delta_ma"]))
self.add_reaction(
Reaction(name="r8",
reactants={'Ma': 1},
products={
'A': 1,
'Ma': 1
},
rate=self.listOfParameters["beta_a"]))
self.add_reaction(
Reaction(name="r9",
reactants={'Da_prime': 1},
products={
'Da_prime': 1,
'A': 1
},
rate=self.listOfParameters["theta_a"]))
self.add_reaction(
Reaction(name="r10",
reactants={'Dr_prime': 1},
products={
'Dr_prime': 1,
'A': 1
},
rate=self.listOfParameters["theta_a"]))
self.add_reaction(
Reaction(name="r11",
reactants={'A': 1},
products={},
rate=self.listOfParameters["gamma_c"]))
self.add_reaction(
Reaction(name="r12",
reactants={
'A': 1,
'R': 1
},
products={'C': 1},
rate=self.listOfParameters["gamma_c"]))
self.add_reaction(
Reaction(name="r13",
reactants={'Dr_prime': 1},
products={
'Dr_prime': 1,
'Mr': 1
},
rate=self.listOfParameters["alpha_r_prime"]))
self.add_reaction(
Reaction(name="r14",
reactants={'Dr': 1},
products={
'Dr': 1,
'Mr': 1
},
rate=self.listOfParameters["alpha_r"]))
self.add_reaction(
Reaction(name="r15",
reactants={'Mr': 1},
products={},
rate=self.listOfParameters["delta_mr"]))
self.add_reaction(
Reaction(name="r16",
reactants={'Mr': 1},
products={
'Mr': 1,
'R': 1
},
rate=self.listOfParameters["beta_r"]))
self.add_reaction(
Reaction(name="r17",
reactants={'R': 1},
products={},
rate=self.listOfParameters["delta_r"]))
self.add_reaction(
Reaction(name="r18",
reactants={'C': 1},
products={'R': 1},
rate=self.listOfParameters["delta_a"]))
# Timespan
self.timespan(np.linspace(0, 200, 201))
def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="LacOperon")
# Species
s1 = Species(name='MR', initial_value=0)
s2 = Species(name='R', initial_value=0)
s3 = Species(name='R2', initial_value=0)
s4 = Species(name='O', initial_value=1)
s5 = Species(name='R2O', initial_value=0)
s6 = Species(name='I', initial_value=0)
s7 = Species(name='Iex', initial_value=0)
s8 = Species(name='I2R2', initial_value=0)
s9 = Species(name='MY', initial_value=0)
s10 = Species(name='Y', initial_value=0)
s11 = Species(name='YIex', initial_value=0)
s12 = Species(name='Ytot', initial_value=0)
self.add_species([s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12])
# Parameters
k1 = Parameter(name='k1', expression=0.111)
k2 = Parameter(name='k2', expression=15.0)
k3 = Parameter(name='k3', expression=103.8)
k4 = Parameter(name='k4', expression=0.001)
k5 = Parameter(name='k5', expression=1992.7)
k6 = Parameter(name='k6', expression=2.40)
k7 = Parameter(name='k7', expression=1.293e-6)
k8 = Parameter(name='k8', expression=12.0)
k9 = Parameter(name='k9', expression=1.293e-6)
k10 = Parameter(name='k10', expression=9963.2)
k11 = Parameter(name='k11', expression=0.50)
k12 = Parameter(name='k12', expression=0.010)
k13 = Parameter(name='k13', expression=30)
k14 = Parameter(name='k14', expression=0.249)
k15 = Parameter(name='k15', expression=0.10)
k16 = Parameter(name='k16', expression=60000)
k17 = Parameter(name='k17', expression=0.920)
k18 = Parameter(name='k18', expression=0.920)
k19 = Parameter(name='k19', expression=0.462)
k20 = Parameter(name='k20', expression=0.462)
k21 = Parameter(name='k21', expression=0.20)
k22 = Parameter(name='k22', expression=0.20)
k23 = Parameter(name='k23', expression=0.20)
k24 = Parameter(name='k24', expression=0.20)
k25 = Parameter(name='k25', expression=0.20)
self.add_parameter([
k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12, k13, k14, k15,
k16, k17, k18, k19, k20, k21, k22, k23, k24, k25
])
# Reactions
j1 = Reaction(name="j1", reactants={s12: 1}, products={s1: 1}, rate=k1)
j2 = Reaction(name="j2",
reactants={s1: 1},
products={
s1: 1,
s2: 1
},
rate=k2)
j3 = Reaction(name="j3", reactants={s2: 2}, products={s3: 1}, rate=k3)
j4 = Reaction(name="j4", reactants={s3: 1}, products={s2: 2}, rate=k4)
j5 = Reaction(name="j5",
reactants={
s3: 1,
s4: 1
},
products={s5: 1},
rate=k5)
j6 = Reaction(name="j6",
reactants={s5: 1},
products={
s3: 1,
s4: 1
},
rate=k6)
j7 = Reaction(name="j7",
reactants={
s6: 2,
s3: 1
},
products={s8: 1},
propensity_function=
'((3e-7)/((8e-16)*(6.0221367e14))**(2))*(R2)*(I)*(I-1)')
j8 = Reaction(name="j8",
reactants={s8: 1},
products={
s6: 2,
s3: 1
},
rate=k8)
j9 = Reaction(name="j9",
reactants={
s6: 2,
s5: 1
},
products={
s8: 1,
s4: 1
},
propensity_function=
'((3e-7)/((8e-16)*(6.0221367e14))**(2))*(R2O)*(I)*(I-1)')
j10 = Reaction(name="j10",
reactants={
s8: 1,
s4: 1
},
products={
s6: 2,
s5: 1
},
rate=k10)
j11 = Reaction(name="j11",
reactants={s4: 1},
products={
s4: 1,
s9: 1
},
rate=k11)
j12 = Reaction(name="j12",
reactants={s5: 1},
products={
s5: 1,
s9: 1
},
rate=k12)
j13 = Reaction(name="j13",
reactants={s9: 1},
products={
s9: 1,
s10: 1
},
rate=k13)
j14 = Reaction(name="j14",
reactants={
s10: 1,
s7: 1
},
products={s11: 1},
rate=k14)
j15 = Reaction(name="j15",
reactants={s11: 1},
products={
s10: 1,
s7: 1
},
rate=k15)
j16 = Reaction(name="j16",
reactants={s11: 1},
products={
s10: 1,
s6: 1
},
rate=k16)
j17 = Reaction(name="j17",
reactants={s7: 1},
products={s6: 1},
rate=k17)
j18 = Reaction(name="j18",
reactants={s6: 1},
products={s7: 1},
rate=k18)
j19 = Reaction(name="j19",
reactants={s1: 1},
products={s12: 1},
rate=k19)
j20 = Reaction(name="j20",
reactants={s9: 1},
products={s12: 1},
rate=k20)
j21 = Reaction(name="j21",
reactants={s2: 1},
products={s12: 1},
rate=k21)
j22 = Reaction(name="j22",
reactants={s3: 1},
products={s12: 1},
rate=k22)
j23 = Reaction(name="j23",
reactants={s10: 1},
products={s12: 1},
rate=k23)
j24 = Reaction(name="j24",
reactants={s11: 1},
products={s6: 1},
rate=k24)
j25 = Reaction(name="j25",
reactants={s8: 1},
products={s6: 2},
rate=k25)
self.add_reaction([
j1, j2, j3, j4, j5, j6, j7, j8, j9, j10, j11, j12, j13, j14, j15,
j16, j17, j18, j19, j20, j21, j22, j23, j24, j25
])
self.timespan(np.linspace(0, 100, 10))
def __init__(self, parameter_values=None):
Model.__init__(self, name="VilarOscillator")
self.volume = 1
# Parameters
alphaA = Parameter(name="alphaA", expression=50)
alphaA_prime = Parameter(name="alphaA_prime", expression=500)
alphaR = Parameter(name="alphaR", expression=0.01)
alphaR_prime = Parameter(name="alphaR_prime", expression=50)
betaA = Parameter(name="betaA", expression=50)
betaR = Parameter(name="betaR", expression=5)
deltaMA = Parameter(name="deltaMA", expression=10)
deltaMR = Parameter(name="deltaMR", expression=0.5)
deltaA = Parameter(name="deltaA", expression=1)
deltaR = Parameter(name="deltaR", expression=0.2)
gammaA = Parameter(name="gammaA", expression=1)
gammaR = Parameter(name="gammaR", expression=1)
gammaC = Parameter(name="gammaC", expression=2)
thetaA = Parameter(name="thetaA", expression=50)
thetaR = Parameter(name="thetaR", expression=100)
self.add_parameter([
alphaA, alphaA_prime, alphaR, alphaR_prime, betaA, betaR, deltaMA,
deltaMR, deltaA, deltaR, gammaA, gammaR, gammaC, thetaA, thetaR
])
# Species
Da = Species(name="Da", initial_value=1, mode="discrete")
Da_prime = Species(name="Da_prime", initial_value=0, mode="discrete")
Ma = Species(name="Ma", initial_value=0, mode="discrete")
Dr = Species(name="Dr", initial_value=1, mode="discrete")
Dr_prime = Species(name="Dr_prime", initial_value=0, mode="discrete")
Mr = Species(name="Mr", initial_value=0, mode="discrete")
C = Species(name="C", initial_value=0, mode="discrete")
A = Species(name="A", initial_value=0, mode="discrete")
R = Species(name="R", initial_value=0, mode="discrete")
self.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])
# Reactions
r1 = Reaction(name="r1",
reactants={
'A': 1,
'R': 1
},
products={'C': 1},
rate="gammaC")
r2 = Reaction(name="r2",
reactants={'A': 1},
products={},
rate="deltaA")
r3 = Reaction(name="r3",
reactants={'C': 1},
products={'R': 1},
rate="deltaA")
r4 = Reaction(name="r4",
reactants={'R': 1},
products={},
rate="deltaR")
r5 = Reaction(name="r5",
reactants={
'A': 1,
'Da': 1
},
products={'Da_prime': 1},
rate="gammaA")
r6 = Reaction(name="r6",
reactants={'Da_prime': 1},
products={
'A': 1,
'Da': 1
},
rate="thetaA")
r7 = Reaction(name="r7",
reactants={'Da': 1},
products={
'Da': 1,
'Ma': 1
},
rate="alphaA")
r8 = Reaction(name="r8",
reactants={'Da_prime': 1},
products={
'Da_prime': 1,
'Ma': 1
},
rate="alphaA_prime")
r9 = Reaction(name="r9",
reactants={'Ma': 1},
products={},
rate="deltaMA")
r10 = Reaction(name="r10",
reactants={'Ma': 1},
products={
'A': 1,
'Ma': 1
},
rate="betaA")
r11 = Reaction(name="r11",
reactants={
'A': 1,
'Dr': 1
},
products={'Dr_prime': 1},
rate="gammaR")
r12 = Reaction(name="r12",
reactants={'Dr_prime': 1},
products={
'A': 1,
'Dr': 1
},
rate="thetaR")
r13 = Reaction(name="r13",
reactants={'Dr': 1},
products={
'Dr': 1,
'Mr': 1
},
rate="alphaR")
r14 = Reaction(name="r14",
reactants={'Dr_prime': 1},
products={
'Dr_prime': 1,
'Mr': 1
},
rate="alphaR_prime")
r15 = Reaction(name="r15",
reactants={'Mr': 1},
products={},
rate="deltaMR")
r16 = Reaction(name="r16",
reactants={'Mr': 1},
products={
'Mr': 1,
'R': 1
},
rate="betaR")
self.add_reaction([
r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15,
r16
])
# Timespan
self.timespan(np.linspace(0, 200, 201))
def __init__(self, parameter_values=None):
Model.__init__(self, name="test1")
self.volume = 1
# Parameters
self.add_parameter(Parameter(name="k1", expression="0.5"))
self.add_parameter(Parameter(name="k2", expression="3.2e-15"))
# Variables
self.add_species(
Species(name="s1", initial_value=8000, mode="continuous"))
self.add_species(Species(name="s2", initial_value=0,
mode="continuous"))
# Reactions
self.add_reaction(
Reaction(name="r1",
reactants={'s1': 1},
products={'s2': 1},
rate=self.listOfParameters["k2"]))
self.add_reaction(
Reaction(name="r2",
reactants={'s1': 1},
products={},
rate=self.listOfParameters["k1"]))
self.add_reaction(
Reaction(name="r3",
reactants={'s1': 2},
products={'s1': 3},
propensity_function="sin(1)"))
# Event Triggers
e1_trig = EventTrigger(expression="t > 50",
initial_value=True,
persistent=True)
# Event Assignments
e1_assign_1 = EventAssignment(variable="s1", expression="2000")
# Events
self.add_event(
Event(name="e1",
trigger=e1_trig,
assignments=[e1_assign_1],
delay="t - 40",
priority="0",
use_values_from_trigger_time=True))
# Rate Rules
self.add_rate_rule(
RateRule(name="rr1", formula="sin(0.5)", variable="s1"))
# Assignment Rules
self.add_assignment_rule(
AssignmentRule(name="rr2", formula="8000/(s1+1)", variable="s2"))
# Function Definitions
self.add_function_definition(
FunctionDefinition(name="multiply",
function="x * y",
args=['x', 'y']))
# Timespan
self.timespan(np.arange(0, 20, 0.05))
def __init__(self, input1, input2, weight1, weight2, output, record_keeper, target, penalty, time, k_array):
self.input1 = input1
self.input2 = input2
self.weight1 = weight1
self.weight2 = weight2
self.output = output
self.record_keeper = record_keeper
self.target = target
self.penalty = penalty
self.time = time
self.k_array = k_array
system_volume = 100
constant = 1
Model.__init__(self, name="Output_NN", volume=system_volume)
x1 = Species(name='input 1', initial_value=int(self.input1))
x2 = Species(name='input 2', initial_value=int(self.input2))
w1 = Species(name='weight 1', initial_value=int(self.weight1))
w2 = Species(name='weight 2', initial_value=int(self.weight2))
y = Species(name='output', initial_value=int(self.output))
xy = Species(name='record_keeper', initial_value=int(self.record_keeper))
wPlus = Species(name='positive weight error', initial_value=0)
wMinus = Species(name='negative weight error', initial_value=0)
sL = Species(name='learning_signal', initial_value=constant*system_volume)
wNeg = Species(name='weight annihilator', initial_value=0)
yhat = Species(name='target', initial_value=int(self.target*system_volume))
ePlus = Species(name='positive error', initial_value=0)
eMinus = Species(name='negative error', initial_value=0)
p = Species(name='penalty', initial_value=int(self.penalty*system_volume))
pNeg1 = Species(name='hidden 1 neg penalty', initial_value=0)
pPos1 = Species(name='hidden 1 pos penalty', initial_value=0)
pNeg2 = Species(name='hidden 2 neg penalty', initial_value=0)
pPos2 = Species(name='hidden 2 pos penalty', initial_value=0)
self.add_species([x1, x2, w1, w2, y, xy, wPlus, wMinus, sL, wNeg, yhat, ePlus, eMinus, p, pNeg1, pPos1, pNeg2, pPos2])
k1 = Parameter(name='k1', expression=self.k_array[6])
k2 = Parameter(name='k2', expression=self.k_array[7])
k3 = Parameter(name='k3', expression=self.k_array[8])
k4 = Parameter(name='k4', expression=self.k_array[9])
k5 = Parameter(name='k5', expression=self.k_array[10])
k6 = Parameter(name='k6', expression=self.k_array[11])
k7 = Parameter(name='k7', expression=self.k_array[12])
k8 = Parameter(name='k8', expression=self.k_array[13])
k9 = Parameter(name='k9', expression=self.k_array[14])
self.add_parameter([k1, k2, k3, k4, k5, k6, k7, k8, k9])
rxn11 = Reaction(name='input 1 to output', reactants={x1: 1, w1: 1}, products={y: 1, xy: 1, w1: 1}, rate=k1)
rxn12 = Reaction(name='input 2 to output', reactants={x2: 1, w2: 1}, products={y: 1, xy: 1, w2: 1}, rate=k1)
rxn21 = Reaction(name='x1 annihilation', reactants={x1: 1, y: 1}, products={}, rate=k2)
rxn22 = Reaction(name='x2 annihilation', reactants={x2: 1, y: 1}, products={}, rate=k2)
rxn3 = Reaction(name='error', reactants={y: 1, yhat: 1}, products={}, rate=k3)
rxn41 = Reaction(name='create positive error', reactants={yhat: 1, sL: 1}, products={ePlus: 1, sL: 1}, rate=k4)
rxn42 = Reaction(name='create positive weight error', reactants={p: 1, ePlus: 1}, products={ePlus: 2, wPlus: 1}, rate=k4)
rxn51 = Reaction(name='create negative error', reactants={y: 1, sL: 1}, products={eMinus: 1, sL: 1}, rate=k5)
rxn52 = Reaction(name='create negative weight error', reactants={p: 1, eMinus: 1}, products={eMinus: 2, wMinus: 1}, rate=k5)
rxn6 = Reaction(name='annihilation of error species', reactants={ePlus: 1, eMinus: 1}, products={}, rate=k6)
rxn7 = Reaction(name='positive weight adjustment', reactants={wPlus: 1, xy: 1}, products={w1: 1, w2: 1, xy: 1}, rate=k7)
rxn81 = Reaction(name='negative weight adjustment 1', reactants={wMinus: 1, xy: 1}, products={wNeg: 1, xy: 1}, rate=k8)
rxn82 = Reaction(name='negative weight adjustment 2', reactants={wNeg: 1, w1: 1}, products={}, rate=k8)
rxn83 = Reaction(name='negative weight adjustment 3', reactants={wNeg: 1, w2: 1}, products={}, rate=k8)
rxn911 = Reaction(name='create neg hidden penalty 1', reactants={wMinus: 1, w1: 1}, products={pNeg1: 1, w1: 1}, rate=k9)
rxn912 = Reaction(name='create pos hidden penalty 1', reactants={wPlus: 1, w1: 1}, products={pPos1: 1, w1: 1}, rate=k9)
rxn921 = Reaction(name='create neg hidden penalty 2', reactants={wMinus: 1, w2: 1}, products={pNeg2: 1, w2: 1}, rate=k9)
rxn922 = Reaction(name='create pos hidden penalty 2', reactants={wPlus: 1, w2: 1}, products={pPos2: 1, w2: 1}, rate=k9)
self.add_reaction([rxn11, rxn12, rxn21, rxn22, rxn3, rxn41, rxn42, rxn51, rxn52, rxn6, rxn7, rxn81, rxn82, rxn83, rxn911, rxn912, rxn921, rxn922])
self.timespan(self.time)
def __init__(self, parameter_values=None):
# initialize Model
Model.__init__(self, name="VilarOscillator")
# parameters
alpha_a = Parameter(name='alpha_a', expression=50.0)
alpha_a_prime = Parameter(name='alpha_a_prime', expression=500.0)
alpha_r = Parameter(name='alpha_r', expression=0.01)
alpha_r_prime = Parameter(name='alpha_r_prime', expression=50.0)
beta_a = Parameter(name='beta_a', expression=50.0)
beta_r = Parameter(name='beta_r', expression=5.0)
delta_ma = Parameter(name='delta_ma', expression=10.0)
delta_mr = Parameter(name='delta_mr', expression=0.5)
delta_a = Parameter(name='delta_a', expression=1.0)
delta_r = Parameter(name='delta_r', expression=0.2)
gamma_a = Parameter(name='gamma_a', expression=1.0)
gamma_r = Parameter(name='gamma_r', expression=1.0)
gamma_c = Parameter(name='gamma_c', expression=2.0)
Theta_a = Parameter(name='Theta_a', expression=50.0)
Theta_r = Parameter(name='Theta_r', expression=100.0)
self.add_parameter([
alpha_a, alpha_a_prime, alpha_r, alpha_r_prime, beta_a, beta_r,
delta_ma, delta_mr, delta_a, delta_r, gamma_a, gamma_r, gamma_c,
Theta_a, Theta_r
])
# Species
Da = Species(name='Da', initial_value=1)
Da_prime = Species(name='Da_prime', initial_value=0)
Ma = Species(name='Ma', initial_value=0)
Dr = Species(name='Dr', initial_value=1)
Dr_prime = Species(name='Dr_prime', initial_value=0)
Mr = Species(name='Mr', initial_value=0)
C = Species(name='C', initial_value=10)
A = Species(name='A', initial_value=10)
R = Species(name='R', initial_value=10)
self.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])
# reactions
s_Da = Reaction(name="s_Da",
reactants={Da_prime: 1},
products={Da: 1},
rate=Theta_a)
s_Da_prime = Reaction(name="s_Da_prime",
reactants={
Da: 1,
A: 1
},
products={Da_prime: 1},
rate=gamma_a)
s_Dr = Reaction(name="s_Dr",
reactants={Dr_prime: 1},
products={Dr: 1},
rate=Theta_r)
s_Dr_prime = Reaction(name="s_Dr_prime",
reactants={
Dr: 1,
A: 1
},
products={Dr_prime: 1},
rate=gamma_r)
s_Ma1 = Reaction(name="s_Ma1",
reactants={Da_prime: 1},
products={
Da_prime: 1,
Ma: 1
},
rate=alpha_a_prime)
s_Ma2 = Reaction(name="s_Ma2",
reactants={Da: 1},
products={
Da: 1,
Ma: 1
},
rate=alpha_a)
a_Ma = Reaction(name="a_Ma",
reactants={Ma: 1},
products={},
rate=delta_ma)
s_A1 = Reaction(name="s_A1",
reactants={Ma: 1},
products={
A: 1,
Ma: 1
},
rate=beta_a)
s_A2 = Reaction(name="S_A2",
reactants={Da_prime: 1},
products={
Da_prime: 1,
A: 1
},
rate=Theta_a)
s_A3 = Reaction(name="S_A3",
reactants={Dr_prime: 1},
products={
Dr_prime: 1,
A: 1
},
rate=Theta_a)
a_A = Reaction(name="a_A", reactants={A: 1}, products={}, rate=gamma_c)
s_C = Reaction(name="s_C",
reactants={
A: 1,
R: 1
},
products={C: 1},
rate=gamma_c)
S_Mr1 = Reaction(name="S_Mr1",
reactants={Dr_prime: 1},
products={
Dr_prime: 1,
Mr: 1
},
rate=alpha_r_prime)
S_Mr2 = Reaction(name="S_Mr2",
reactants={Dr: 1},
products={
Dr: 1,
Mr: 1
},
rate=alpha_r)
a_Mr = Reaction(name="a_Mr",
reactants={Mr: 1},
products={},
rate=delta_mr)
s_R1 = Reaction(name="s_R1",
reactants={Mr: 1},
products={
Mr: 1,
R: 1
},
rate=beta_r)
a_R = Reaction(name="a_R", reactants={R: 1}, products={}, rate=delta_r)
s_r2 = Reaction(name="s_r2",
reactants={C: 1},
products={R: 1},
rate=delta_a)
self.add_reaction([
s_Da, s_Da_prime, s_Dr, s_Dr_prime, s_Ma1, s_Ma2, a_Ma, s_A1, s_A2,
s_A3, a_A, s_C, S_Mr1, S_Mr2, a_Mr, s_R1, a_R, s_r2
])
self.timespan(np.linspace(0, 200, 201))