na = rxd.Species([cyt, mem, ecs],
name="na",
d=1,
charge=1,
initial=concentration(15.5, 161.5))
k = rxd.Species([cyt, mem, ecs],
name="k",
d=1,
charge=1,
initial=concentration(142.5, 7.8))
cl = rxd.Species([cyt, mem, ecs],
name="cl",
d=1,
charge=-1,
initial=concentration(6.0, 130))
glial_na = rxd.Parameter([gcyt, gmem], name="na", charge=1,
value=18) # Na in book is 55 for astrocyte
glial_k = rxd.Parameter([gcyt, gmem], name="k", charge=1, value=80)
dump = rxd.Parameter([cyt, mem, gcyt, gmem], name="dump")
ki, ko, nai, nao, cli, clo, gnai, gki = (
k[cyt],
k[ecs],
na[cyt],
na[ecs],
cl[cyt],
cl[ecs],
glial_na[gcyt],
glial_k[gcyt],
)
# i = in cytosol (cyt) o = in extracellular (ecs)
# extracellular oxygen concentration
o2ecs = rxd.Species([ecs], name="o2ecs", initial=oa_bath)
from neuron import h, rxd
from neuron.units import mV, ms
h.load_file("stdrun.hoc")
soma = h.Section(name="soma")
soma.diam = soma.L = 10
# the geometry of the ecs doesn't matter in the constant case
ecs = rxd.Region([soma], name="ecs", nrn_region="o", geometry=rxd.Shell(1, 2))
cyt = rxd.Region([soma], name="cyt", nrn_region="i")
mem = rxd.Region([soma], name="mem", geometry=rxd.membrane())
cao = rxd.Parameter(ecs, charge=2)
ca = rxd.Species(
[cyt],
d=1, # with single section and nseg=1 only affects extracellular
name="ca",
charge=2,
initial=lambda node: 1e-3 if node.region == cyt else 0,
)
e = 1.60217662e-19
scale = 1e-14 / e
# rate constant is in terms of molecules/um2 ms
ca_pump = rxd.MultiCompartmentReaction(
ca[cyt],
cao[ecs],
ca[cyt] * scale,
from neuron.units import mM, ms
sim_time = 100
axon_terminal = h.Section(name="axon_terminal")
#synaptic_cleft = rxd.Region([axon_terminal], nrn_region='o')
axon_terminal_region = rxd.Region([axon_terminal], nrn_region='i')
ca_intra = rxd.Species(axon_terminal_region,
name="ca",
charge=2,
d=0,
initial=5e-4,
atolscale=1e-6)
F = rxd.Species(axon_terminal_region, name="F", initial=0.001)
FA = rxd.Species(axon_terminal_region, name="FA", initial=0)
kb = rxd.Parameter(axon_terminal_region, name="kb", value=1e16 / (mM**4 * ms))
ku = rxd.Parameter(axon_terminal_region, name="ku", value=0.1 / ms)
myreaction = rxd.Reaction(F + 4 * ca_intra, FA, kb,
ku) #, regions=axon_terminal_region)
tvec = h.Vector().record(h._ref_t)
Fvec = h.Vector().record(F.nodes._ref_concentration)
FAvec = h.Vector().record(FA.nodes._ref_concentration)
h.finitialize(-65)
h.continuerun(10)
pyplot.plot(tvec, Fvec)
pyplot.plot(tvec, FAvec)
pyplot.show()
def declare_parameters(**kwargs):
"""enables clean declaration of parameters in top namespace"""
for key in sorted(kwargs):
globals()[key] = rxd.Parameter(r, name=key, value=kwargs[key])
'PDE4', 'PDE4cAMP'
]
windows = [0, 0, 0, 2, 1, 1, 3, 3, 4, 4]
tolscales = [1.0 for i in range(0, len(species))]
print "my_volume = " + str(my_volume) + " l ?= " + str(
dend.L * (dend.diam / 2)**2 * 3.14159265358) + " um3"
specs = []
for ispec in range(0, len(species)):
specs.append(
rxd.Species(cyt,
name='spec' + str(ispec),
charge=0,
initial=initvalues[ispec],
atolscale=tolscales[ispec]))
cAMP_flux_rate = rxd.Parameter(cyt, initial=0)
#specs[0] cAMP
#specs[1] PKA2cAMP
#specs[2] PKA4cAMP
#specs[3] PKA
#specs[4] PKAr
#specs[5] PKAc
#specs[6] AMP
#specs[7] ATP
#specs[8] PDE4
#specs[9] PDE4cAMP
reaction1 = rxd.Reaction(specs[3] + specs[0] * 4 <> specs[2], ks[0], ks[1])
reaction3 = rxd.Reaction(specs[2] <> specs[4] + specs[5] * 2,
ks[4] * specs[2],
ks[5] * specs[4] * specs[5],
na = rxd.Species([cyt, mem, ecs],
name='na',
d=1,
charge=1,
initial=concentration(15.5, 161.5))
k = rxd.Species([cyt, mem, ecs],
name='k',
d=1,
charge=1,
initial=concentration(142.5, 7.8))
cl = rxd.Species([cyt, mem, ecs],
name='cl',
d=1,
charge=-1,
initial=concentration(6.0, 130))
glial_na = rxd.Parameter([gcyt, gmem], name='na', charge=1,
value=18) #Na in book is 55 for astrocyte
glial_k = rxd.Parameter([gcyt, gmem], name='k', charge=1, value=80)
dump = rxd.Parameter([cyt, mem, gcyt, gmem], name='dump')
ki, ko, nai, nao, cli, clo, gnai, gki = k[cyt], k[ecs], na[cyt], na[ecs], cl[
cyt], cl[ecs], glial_na[gcyt], glial_k[gcyt]
#i = in cytosol (cyt) o = in extracellular (ecs)
#extracellular oxygen concentration
o2ecs = rxd.Species([ecs], name='o2ecs', initial=oa_bath)
#STATES-------------------------------------------------------------------------------------------------------------------------
#gating variables (m, h, n)
mgate = rxd.State([cyt, mem], name='mgate', initial=0.00787013592322398)
hgate = rxd.State([cyt, mem], name='hgate', initial=0.9981099795551048)
ngate = rxd.State([cyt, mem], name='ngate', initial=0.02284760152971809)
cyt = rxd.Region(h.allsec(),
nrn_region='i',
geometry=rxd.FractionalVolume(fc, surface_fraction=1))
er = rxd.Region(h.allsec(), geometry=rxd.FractionalVolume(fe))
# this defines a boundary with an area independent of geometry (here there is
# one square micron of area per micron of length)
cyt_er_membrane = rxd.Region(h.allsec(), geometry=rxd.ScalableBorder(1))
# reaction-diffusion definition
ca = rxd.Species([cyt, er], d=caDiff, name='ca', charge=2, initial=caCYT_init)
ip3 = rxd.Species(cyt, d=ip3Diff, initial=ip3_init)
#gip3r - not a rate constant but multiplies k, which is a rate constant (?)
gip3r = rxd.Parameter(
cyt, initial=ip3_notorigin
) # parameters but set magnitude via concentration or value
gserca = rxd.Parameter(cyt, initial=gserca0)
gleak = rxd.Parameter(cyt, initial=gleak0)
# action of IP3 receptor
Kip3 = 0.13 # Kip3 = 0.15
Kact = 0.4
minf = ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + Kip3) / (1000. * ca[cyt] +
Kact)
#ip3r_gate_state = rxd.State(cyt_er_membrane, initial=0.78282068629348611)
ip3r_gate_state = rxd.State(cyt_er_membrane, initial=0.8)
h_gate = ip3r_gate_state[cyt_er_membrane]
k = gip3r[cyt] * (minf * h_gate)**3
ip3r = rxd.MultiCompartmentReaction(ca[er] <> ca[cyt],
k,