IP3R model comparison (Hituri and Linne 2013)

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In this study, four models of IP3R (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected among many to examine their behavior and compare them with experimental data available in literature. The provided MATLAB script (run_IP3R_P0.m) will run the simulations and plot Figure 2A in the paper.
1 . Hituri K, Linne ML (2013) Comparison of models for IP3 receptor kinetics using stochastic simulations. PLoS One 8:e59618 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Channel/Receptor;
Brain Region(s)/Organism: Unknown;
Cell Type(s):
Gap Junctions:
Receptor(s): IP3;
Simulation Environment: STEPS;
Model Concept(s): Ion Channel Kinetics; Calcium waves;
Implementer(s): Hituri, Katri [katri.hituri at gmail.com];
Search NeuronDB for information about:  IP3;
# Katri Hituri
# Script to simulate open probability of IP3R
# Model of Dawson et al. 2003


import ip3r_model_dli as model

import steps.rng as srng
import steps.solver as ssolver
import numpy


#Concentrations for Ca in cytosol
ca_concs = numpy.array([0.001e-6, 0.003e-6, 0.01e-6, 0.02e-6, 0.05e-6, 0.07e-6, 0.10e-6, 0.15e-6, 0.20e-6, 0.25e-6, \
                        0.28e-6, 0.30e-6, 0.33e-6, 0.35e-6, 0.36e-6, 0.38e-6, 0.43e-6, 0.50e-6, 1.00e-6, 1.50e-6, 2.00e-6,\
                        2.50e-6, 5.00e-6, 10.00e-6]) # mol/l

# Solver settings
r = srng.create('mt19937', 1000)
sim = ssolver.Wmdirect(model.mdl, model.cell, r)

# Number of iterations (defines how many times the model is simulated)
NITER = 1 #5000

tpnt = numpy.arange(0.0, 50.01, 0.01)

# array for simulation results
res = numpy.zeros([ca_concs.size, 2])

print 'Simulating the IP3R model of Dawson et al. 2003.'

for i in xrange(ca_concs.size):

	print 'Round', i+1, '/', ca_concs.size
    	temp_res = numpy.zeros([NITER, tpnt.size])
    	for j in xrange(NITER): 
        	sim.setPatchCount('ER_memb', 'R', 1) # number of naive receptor
        	sim.setCompConc('cyt', 'IP3', 10e-6) # [IP3] = 10 uM
        	sim.setCompConc('cyt', 'Ca', ca_concs[i])
        	sim.setCompClamped('cyt', 'Ca', 1)
        	sim.setCompClamped('cyt', 'IP3', 1)
        	for t in xrange(tpnt.size):
            		o1 = sim.getPatchCount('ER_memb', 'O1')
            		o2 = sim.getPatchCount('ER_memb', 'O2')
            		temp_res[j,t] = o1 + o2        
    	temp = numpy.mean(temp_res[:,2501:])
    	res[i,0] = numpy.mean(temp, 0)
    	res[i,1] = numpy.std(temp, 0)

numpy.savetxt('ip3r_dli_op_res.dat', res)
numpy.savetxt('ip3r_dli_op_ca_concs.dat', ca_concs)

print res[:,0]