#########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2013 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2.1
## See the file COPYING.LIB for the full notice.
#########################################################################
import math
import pylab
import numpy
import matplotlib.pyplot as plt
import moose
#diffConst = 10e-12 # m^2/sec
diffConst = 0.0
def makeModel():
model = moose.Neutral( '/model' )
# Make neuronal model. It has no channels, just for geometry
cell = moose.loadModel( './spinyNeuron.p', '/model/cell', 'Neutral' )
# We don't want the cell to do any calculations. Disable everything.
for i in moose.wildcardFind( '/model/cell/##' ):
i.tick = -1
# create container for model
model = moose.element( '/model' )
chem = moose.Neutral( '/model/chem' )
# The naming of the compartments is dicated by the places that the
# chem model expects to be loaded.
compt0 = moose.NeuroMesh( '/model/chem/compt0' )
compt0.separateSpines = 1
compt0.geometryPolicy = 'cylinder'
compt1 = moose.SpineMesh( '/model/chem/compt1' )
moose.connect( compt0, 'spineListOut', compt1, 'spineList', 'OneToOne' )
compt2 = moose.PsdMesh( '/model/chem/compt2' )
moose.connect( compt0, 'psdListOut', compt2, 'psdList', 'OneToOne' )
#reacSystem = moose.loadModel( 'simpleOsc.g', '/model/chem', 'ee' )
makeChemModel( compt0 ) # Populate all 3 compts with the chem system.
makeChemModel( compt1 )
makeChemModel( compt2 )
compt0.diffLength = 2e-6 # This will be over 100 compartments.
# This is the magic command that configures the diffusion compartments.
compt0.subTreePath = cell.path + "/##"
moose.showfields( compt0 )
# Build the solvers. No need for diffusion in this version.
ksolve0 = moose.Ksolve( '/model/chem/compt0/ksolve' )
ksolve1 = moose.Gsolve( '/model/chem/compt1/ksolve' )
ksolve2 = moose.Gsolve( '/model/chem/compt2/ksolve' )
#dsolve0 = moose.Dsolve( '/model/chem/compt0/dsolve' )
#dsolve1 = moose.Dsolve( '/model/chem/compt1/dsolve' )
#dsolve2 = moose.Dsolve( '/model/chem/compt2/dsolve' )
stoich0 = moose.Stoich( '/model/chem/compt0/stoich' )
stoich1 = moose.Stoich( '/model/chem/compt1/stoich' )
stoich2 = moose.Stoich( '/model/chem/compt2/stoich' )
# Configure solvers
stoich0.compartment = compt0
stoich1.compartment = compt1
stoich2.compartment = compt2
stoich0.ksolve = ksolve0
stoich1.ksolve = ksolve1
stoich2.ksolve = ksolve2
#stoich0.dsolve = dsolve0
#stoich1.dsolve = dsolve1
#stoich2.dsolve = dsolve2
stoich0.path = '/model/chem/compt0/#'
stoich1.path = '/model/chem/compt1/#'
stoich2.path = '/model/chem/compt2/#'
assert( stoich0.numVarPools == 3 )
assert( stoich0.numProxyPools == 0 )
assert( stoich0.numRates == 4 )
assert( stoich1.numVarPools == 3 )
assert( stoich1.numProxyPools == 0 )
#assert( stoich1.numRates == 4 )
assert( stoich2.numVarPools == 3 )
assert( stoich2.numProxyPools == 0 )
#assert( stoich2.numRates == 4 )
#dsolve0.buildNeuroMeshJunctions( dsolve1, dsolve2 )
stoich0.buildXreacs( stoich1 )
stoich1.buildXreacs( stoich2 )
stoich0.filterXreacs()
stoich1.filterXreacs()
stoich2.filterXreacs()
moose.element( '/model/chem/compt2/a[0]' ).concInit *= 1.5
# Create the output tables
num = compt0.numDiffCompts - 1
graphs = moose.Neutral( '/model/graphs' )
makeTab( 'a_soma', '/model/chem/compt0/a[0]' )
makeTab( 'b_soma', '/model/chem/compt0/b[0]' )
makeTab( 'a_apical', '/model/chem/compt0/a[' + str( num ) + ']' )
makeTab( 'b_apical', '/model/chem/compt0/b[' + str( num ) + ']' )
makeTab( 'a_spine', '/model/chem/compt1/a[5]' )
makeTab( 'b_spine', '/model/chem/compt1/b[5]' )
makeTab( 'a_psd', '/model/chem/compt2/a[5]' )
makeTab( 'b_psd', '/model/chem/compt2/b[5]' )
def makeTab( plotname, molpath ):
tab = moose.Table2( '/model/graphs/' + plotname ) # Make output table
# connect up the tables
moose.connect( tab, 'requestOut', moose.element( molpath ), 'getConc' );
def makeDisplay():
plt.ion()
fig = plt.figure( figsize=(10,12) )
dend = fig.add_subplot( 411 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'Dend voxel #' )
plt.legend()
timeLabel = plt.text(200, 0.5, 'time = 0')
spine = fig.add_subplot( 412 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'Spine voxel #' )
plt.legend()
psd = fig.add_subplot( 413 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'PSD voxel #' )
plt.legend()
timeSeries = fig.add_subplot( 414 )
timeSeries.set_ylim( 0, 0.6 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'time (seconds)' )
plt.legend()
a = moose.vec( '/model/chem/compt0/a' )
b = moose.vec( '/model/chem/compt0/b' )
line1, = dend.plot( list(range( len( a ))), a.conc, label='a' )
line2, = dend.plot( list(range( len( b ))), b.conc, label='b' )
dend.set_ylim( 0, 0.6 )
a = moose.vec( '/model/chem/compt1/a' )
b = moose.vec( '/model/chem/compt1/b' )
line3, = spine.plot( list(range( len( a ))), a.conc, label='a' )
line4, = spine.plot( list(range( len( b ))), b.conc, label='b' )
spine.set_ylim( 0, 0.6 )
a = moose.vec( '/model/chem/compt2/a' )
b = moose.vec( '/model/chem/compt2/b' )
line5, = psd.plot( list(range( len( a ))), a.conc, label='a' )
line6, = psd.plot( list(range( len( b ))), b.conc, label='b' )
psd.set_ylim( 0, 0.6 )
fig.canvas.draw()
return ( timeSeries, dend, spine, psd, fig, line1, line2, line3, line4, line5, line6, timeLabel )
def updateDisplay( plotlist ):
a = moose.vec( '/model/chem/compt0/a' )
b = moose.vec( '/model/chem/compt0/b' )
plotlist[5].set_ydata( a.conc )
plotlist[6].set_ydata( b.conc )
print(('compt0:', min(a.n), max(a.n), min(b.n), max(b.n)))
a = moose.vec( '/model/chem/compt1/a' )
b = moose.vec( '/model/chem/compt1/b' )
plotlist[7].set_ydata( a.conc )
plotlist[8].set_ydata( b.conc )
print(('compt1:', min(a.n), max(a.n), min(b.n), max(b.n)))
a = moose.vec( '/model/chem/compt2/a' )
b = moose.vec( '/model/chem/compt2/b' )
plotlist[9].set_ydata( a.conc )
plotlist[10].set_ydata( b.conc )
plotlist[4].canvas.draw()
print(('compt2:', min(a.n), max(a.n), min(b.n), max(b.n)))
def finalizeDisplay( plotlist, cPlotDt ):
for x in moose.wildcardFind( '/model/graphs/#[ISA=Table2]' ):
pos = numpy.arange( 0, x.vector.size, 1 ) * cPlotDt
line1, = plotlist[0].plot( pos, x.vector, label=x.name )
plotlist[4].canvas.draw()
print( "Hit any key to exit" )
try:
raw_input( )
except NameError as e:
input( )
[docs]def makeChemModel( compt ):
"""
This function setus up a simple oscillatory chemical system within
the script. The reaction system is::
s ---a---> a // s goes to a, catalyzed by a.
s ---a---> b // s goes to b, catalyzed by a.
a ---b---> s // a goes to s, catalyzed by b.
b -------> s // b is degraded irreversibly to s.
in sum, **a** has a positive feedback onto itself and also forms **b**.
**b** has a negative feedback onto **a**.
Finally, the diffusion constant for **a** is 1/10 that of **b**.
"""
# create container for model
concA = 1 # millimolar
# create molecules and reactions
a = moose.Pool( compt.path + '/a' )
b = moose.Pool( compt.path + '/b' )
s = moose.Pool( compt.path + '/s' )
e1 = moose.MMenz( compt.path + '/e1' )
e2 = moose.MMenz( compt.path + '/e2' )
e3 = moose.MMenz( compt.path + '/e3' )
r1 = moose.Reac( compt.path + '/r1' )
a.concInit = 0.1
b.concInit = 0.1
s.concInit = 1
moose.connect( e1, 'sub', s, 'reac' )
moose.connect( e1, 'prd', a, 'reac' )
moose.connect( a, 'nOut', e1, 'enzDest' )
e1.Km = 1
e1.kcat = 1
moose.connect( e2, 'sub', s, 'reac' )
moose.connect( e2, 'prd', b, 'reac' )
moose.connect( a, 'nOut', e2, 'enzDest' )
e2.Km = 1
e2.kcat = 0.5
moose.connect( e3, 'sub', a, 'reac' )
moose.connect( e3, 'prd', s, 'reac' )
moose.connect( b, 'nOut', e3, 'enzDest' )
e3.Km = 0.1
e3.kcat = 1
moose.connect( r1, 'sub', b, 'reac' )
moose.connect( r1, 'prd', s, 'reac' )
r1.Kf = 0.3 # 1/sec
r1.Kb = 0 # 1/sec
# Assign parameters
a.diffConst = diffConst/10
b.diffConst = diffConst
s.diffConst = 0
[docs]def main():
"""
This example illustrates how to define a kinetic model embedded in
the branching pseudo-1-dimensional geometry of a neuron. The model
oscillates in space and time due to a Turing-like reaction-diffusion
mechanism present in all compartments. For the sake of this demo,
the initial conditions are set up slightly different on the PSD
compartments, so as to break the symmetry and initiate oscillations
in the spines.
This example uses an external electrical model file with basal
dendrite and three branches on
the apical dendrite. One of those branches has a dozen or so spines.
In this example we build an identical model in each compartment, using
the makeChemModel function. One could readily define a system with
distinct reactions in each compartment.
The model is set up to run using the Ksolve for integration and the
Dsolve for handling diffusion.
The display has four parts:
a. animated line plot of concentration against main compartment#.
b. animated line plot of concentration against spine compartment#.
c. animated line plot of concentration against psd compartment#.
d. time-series plot that appears after the simulation has
ended. The plot is for the last (rightmost) compartment.
"""
chemdt = 0.1 # Tested various dts, this is reasonable.
diffdt = 0.01
plotdt = 1
animationdt = 5
runtime = 200
makeModel()
plotlist = makeDisplay()
# Schedule the whole lot - autoscheduling already does this.
'''
for i in range( 11, 17 ):
moose.setClock( i, chemdt ) # for the chem objects
moose.setClock( 10, diffdt ) # for the diffusion
moose.setClock( 18, plotdt ) # for the output tables.
'''
moose.reinit()
for i in range( 0, runtime, animationdt ):
moose.start( animationdt )
plotlist[11].set_text( "time = %d" % i )
updateDisplay( plotlist )
finalizeDisplay( plotlist, plotdt )
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
main()