Resource competition in growing neurites (Hjorth et al 2014)

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Accession:152788
Computer model of neurite outgrowth in a simplified neuron. A growth limiting resource is produced in the soma, transported through the neurites and consumed at the growth cones.
Reference:
1 . Hjorth JJ, van Pelt J, Mansvelder HD, van Ooyen A (2014) Competitive dynamics during resource-driven neurite outgrowth. PLoS One 9:e86741 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Simplified Models; Development;
Implementer(s): Hjorth, Johannes [hjorth at csc.kth.se];
import sys
import getopt

import multiprocessing
import subprocess

import os.path

import math
import numpy
import clock
import substance
import compartment
import saveData

from solver import Solver
# from solverDense import Solver
from point import Point

from experiment import Experiment
from experimentLoaderPredictSpeed import ExperimentLoaderPredictSpeed

def main():

  # Add error checking
  if(len(sys.argv) < 2):
    print("Usage: " + sys.argv[0] + " summaryFile\n")
    exit()

  summaryFile = sys.argv[1]

  print("Summary file: " + summaryFile + "\n")

  fp = open(summaryFile,'r')

  jobList = []
  results = []

  for line in fp:

    words = line.split()
    jobID = words[0]
    infoFile = words[1]

    jobList.append(infoFile)

  fp.close()

  print jobList

  pool = multiprocessing.Pool(None)
  r = pool.map_async(runSim, jobList, callback=results.append)
  r.wait()
  print results

  print "All jobs done."


def runSim(infoFile):

  try:
    print "Processing ", infoFile

    maxElem = 5 #51 #51 #2010 #1010

    clockDt = 10.0 # 2000.0 #50.0
    outputDt = 500.0
    maxCompLen = 10e-6 # 200e-6 #9e-6 # 2.5e-6
    minCompLen = 1e-6 # 50e-6 #1e-6 #0.5e-6

    solver = Solver(maxElem,clockDt,outputDt,maxCompLen,minCompLen)
    experiment = ExperimentLoaderPredictSpeed(solver,infoFile)

    if(not experiment.aborted):

      solver.init(True)
      solver.run()
      solver.finish()

      print "Data point done: " + infoFile

      return True

    else:

      return False

  except:
    # Catch any errors that may have occured for the worker to prevent all 
    # other workers from stopping.
    errorFile = infoFile + ".errorlog.txt"
    fp = open(errorFile,"w")
    fp.write("Worker failed:")
    fp.write(sys.exc_info()[0])
    fp.close()

    print "Unexpected error:", sys.exc_info()[0]


if __name__ == "__main__":
    main()

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