Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)

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Accession:189154
" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
Reference:
1 . Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016) Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 7:157 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Molecular Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex U1 pyramidal intratelencephalic L2-6 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Neocortex layer 4 neuron; Neocortex layer 2-3 interneuron; Neocortex layer 4 interneuron; Neocortex layer 5 interneuron; Neocortex layer 6a interneuron;
Channel(s): I A; I h; I_SERCA; Ca pump; I K,Ca; I Calcium; I L high threshold; I T low threshold; I N; I_KD; I M; I Na,t;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; mGluR;
Gene(s): HCN1;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Oscillations; Activity Patterns; Beta oscillations; Reaction-diffusion; Calcium dynamics; Pathophysiology; Multiscale;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Dura-Bernal, Salvador [salvadordura at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 interneuron basket PV cell; Neocortex U1 pyramidal intratelencephalic L2-6 cell; GabaA; GabaB; AMPA; mGluR; I Na,t; I L high threshold; I N; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I_SERCA; I_KD; Ca pump; Gaba; Glutamate;
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dystdemo
readme.txt
cagk.mod
cal.mod *
calts.mod *
can.mod *
cat.mod *
gabab.mod
h_winograd.mod
HCN1.mod
IC.mod *
icalts.mod *
ihlts.mod *
kap.mod
kcalts.mod *
kdmc.mod
kdr.mod
km.mod *
mglur.mod *
misc.mod *
MyExp2SynBB.mod *
MyExp2SynNMDABB.mod
nax.mod
stats.mod *
vecst.mod *
aux_fun.inc *
conf.py
declist.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
geom.py
ghk.inc *
grvec.hoc
init.hoc
labels.hoc
labels.py *
local.hoc *
misc.h
mpisim.py
netcfg.cfg
nqs.hoc *
nqs.py
nrnoc.hoc *
pyinit.py *
python.hoc *
pywrap.hoc *
simctrl.hoc *
simdat.py
syn.py
syncode.hoc *
vector.py *
xgetargs.hoc *
                            
import ConfigParser
import io

# default config as string
def_config = """
[seed]
iseed = 1234
wseed = 4321
pseed = 4321
[netsyn]
NMAMREE = 0.1
NMAMREI = 0.1
mGLURR = 7.5
GB2R = 7.5
rdmsec = 1
nmfracca = 0.13
[chan]
ihginc = 2.0
iark2fctr = 1.0
iark4 = 0.008
erevh = -30.0
h_lambda = 325.0
h_gbar = 0.0025
fs_h_gbar = 0.00002
lts_h_gbar = 0.15
cagk_gbar = 0.0001
ikc_gkbar = 0.003
nax_gbar = 0.081
kdr_gbar = 0.021
kap_gbar = 0.3
kdmc_gbar = 0.00085
km_gmax = 0.1
cabar = 0.005
lts_cabar = 1.0
[cada]
taur = 5
[run]
indir = data
outdir = data
tstop = 2000.0
dt = 0.1
saveout = 1
simstr = 15dec29_B
statestr = 15apr20_net_S3
dorun = 1
doquit = 0
dodraw = 0
verbose = 0
recdt = 10.0
recvdt = 1.0
binsz = 5
saveconns = 0
[rxd]
CB_frate=5.5
CB_brate=0.0026
CB_init=0.2
gip3 = 120400.0
gserca = 4.0
gleak = 3.0
cacytinit = 100e-6
caerinit = 1.25
caexinit = 0.0
spaceum = 0.0
nsubseg = 0
subsegum = 0.0
v1ryr = 100.0
[net]
scale=1.0
IIGain = 0.1
IEGain = 0.15
EIGainFS = 0.15
EIGainLTS = 0.15
EEGain = 0.25
[stim]
EXGain = 1.0
noise = 1
ip3_stim = 0.0
ip3_stimT = 10000.0
sgrhzNMI = 600.0
sgrhzNME = 300.0
sgrhzEE = 800.0
sgrhzEI = 1600.0
sgrhzIE = 150.0
sgrhzII = 150.0
sgrhzMGLURE = 0.0
sgrhzGB2 = 0.0
"""

# write config file starting with defaults and new entries
# specified in section (sec) , option (opt), and value (val)
# saves to output filepath fn
def writeconf (fn,sec,opt,val):
  conf = ConfigParser.ConfigParser()
  conf.optionxform = str
  conf.readfp(io.BytesIO(def_config)) # start with defaults
  # then change entries by user-specs
  for i in xrange(len(sec)): conf.set(sec[i],opt[i],val[i])
  # write config file
  with open(fn, 'wb') as cfile: conf.write(cfile)

# read config file
def readconf (fn="physiol.cfg"):

  config = ConfigParser.ConfigParser()
  config.optionxform = str
  config.read(fn)

  def conffloat (base,var,defa): # defa is default value
    val = defa
    try: val=config.getfloat(base,var)
    except: pass
    return val

  def confint (base,var,defa):
    val = defa
    try: val=config.getint(base,var)
    except: pass
    return val

  def confstr (base,var,defa):
    val = defa
    try: val = config.get(base,var)
    except: pass
    return val

  d = {}

  d['iseed'] = confint("seed","iseed",1234)
  d['wseed'] = confint("seed","wseed",4321)
  d['pseed'] = confint("seed","pseed",4321)
  d['NMAMREE'] = conffloat("netsyn","NMAMREE",0.1)
  d['NMAMREI'] = conffloat("netsyn","NMAMREI",0.1)
  d['mGLURR'] = conffloat("netsyn","mGLURR",7.5)
  d['GB2R'] = conffloat("netsyn","GB2R",7.5)
  d['nmfracca'] = conffloat("netsyn","nmfracca", 0.13)
  d['rdmsec'] = confint("netsyn","rdmsec", 1)
  d['erevh'] = conffloat("chan","erevh",-30.0)
  d['h_lambda'] = conffloat("chan","h_lambda",325.0)
  d['h_gbar'] = conffloat("chan","h_gbar",0.0025)
  d['fs_h_gbar'] = conffloat("chan","fs_h_gbar",0.00002)
  d['lts_h_gbar'] = conffloat("chan","lts_h_gbar",0.15)
  d['cagk_gbar'] = conffloat("chan","cagk_gbar",0.0001)
  d['ikc_gkbar'] = conffloat("chan","ikc_gkbar",0.003)
  d['nax_gbar'] = conffloat("chan","nax_gbar",0.081)
  d['kdr_gbar'] = conffloat("chan","kdr_gbar",0.021)
  d['kap_gbar'] = conffloat("chan","kap_gbar",0.3)
  d['kdmc_gbar'] = conffloat("chan","kdmc_gbar",0.00085)
  d['km_gmax'] = conffloat("chan","km_gmax",0.1)
  d['ihginc'] = conffloat("chan","ihginc", 2.0)
  d['iark2fctr'] = conffloat("chan", "iark2fctr",1.0)
  d['iark4'] = conffloat("chan", "iark4",0.008)
  d['cabar'] = conffloat("chan","cabar",0.005)
  d['lts_cabar'] = conffloat("chan","lts_cabar",1.0)
  d['taurcada'] = conffloat("cada", "taur", 5.0)
  d['outdir'] = confstr("run","outdir", "data")
  d['indir'] = confstr("run","indir", "data")
  d['tstop'] = conffloat("run","tstop", 2000.0)
  d['dt'] = conffloat("run","dt",0.1)
  d['saveout'] = conffloat("run","saveout",1)
  d['simstr'] = confstr("run","simstr","15dec29_B")
  d['statestr'] = confstr("run","statestr","15apr20_net_S3")
  d['dorun'] = confint("run","dorun",1)
  d['recdt'] = conffloat("run","recdt",10.0)
  d['recvdt'] = conffloat("run","recvdt",1.0)
  d['binsz'] = conffloat("run","binsz",5)

  for k in ['saveconns','doquit','verbose','dodraw']: d[k] = confint("run",k,0)

  d['CB_frate'] = conffloat("rxd","CB_frate", 5.5)
  d['CB_brate'] = conffloat("rxd","CB_brate", 0.0026)
  d['CB_init'] = conffloat("rxd","CB_init", 0.2)
  d['gip3'] = conffloat("rxd","gip3",120400.0)
  d['gserca'] = conffloat("rxd","gserca",4.0)
  d['gleak'] = conffloat("rxd","gleak",3.0)
  d['caerinit'] = conffloat("rxd","caerinit",1.25)
  d['cacytinit'] = conffloat("rxd","cacytinit",100e-6)
  d['caexinit'] = conffloat("rxd","caexinit",0.0)
  d['spaceum'] = conffloat("rxd","spaceum",0.0)
  d['nsubseg'] = confint("rxd","nsubseg",0)
  d['subsegum'] = conffloat("rxd","subsegum",0.0)
  d['v1ryr'] = conffloat("rxd","v1ryr",100.0)
  d['scale'] = conffloat("net","scale",1.0)
  d['IIGain'] = conffloat("net","IIGain",0.1)
  d['IEGain'] = conffloat("net","IEGain",0.15)
  d['EIGainFS'] = conffloat("net","EIGainFS",0.15)
  d['EIGainLTS'] = conffloat("net","EIGainLTS",0.15)
  d['EEGain'] = conffloat("net","EEGain",0.25)
  d['EXGain'] = conffloat("stim","EXGain",1.0)
  d['noise'] = conffloat("stim","noise",1.0)
  d['ip3_stim'] = conffloat("stim","ip3_stim",0.0)
  d['ip3_stimT'] = conffloat("stim","ip3_stimT",10000.0)
  d['sgrhzNME'] = conffloat("stim","sgrhzNME",300.0)
  d['sgrhzNMI'] = conffloat("stim","sgrhzNMI",600.0)
  d['sgrhzEE'] = conffloat("stim","sgrhzEE",800.0)
  d['sgrhzIE'] = conffloat("stim","sgrhzIE",150.0)
  d['sgrhzEI'] = conffloat("stim","sgrhzEI",1600.0)
  d['sgrhzII'] = conffloat("stim","sgrhzII",150.0)
  d['sgrhzMGLURE'] = conffloat("stim","sgrhzMGLURE",0.0)
  d['sgrhzGB2'] = conffloat("stim","sgrhzGB2",0.0)

  return d


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