- Author:
- soroush <ssaf006@aucklanduni.ac.nz>
- Date:
- 2022-09-07 10:24:08+12:00
- Desc:
- adding labels.
- Permanent Source URI:
- https://models.physiomeproject.org/workspace/840/rawfile/996f990613016a37eedc1d35f1a4c298e30002ea/Figure06.py
# To reproduce Figure 1 in the associated Physiome paper,
# execute this script from the command line:
#
# cd [PathToThisFile]
# [PathToOpenCOR]/pythonshell Figure6.py
import matplotlib.pyplot as plt
import opencor as opencor
import numpy as np
simulation = opencor.open_simulation("model.sedml")
data = simulation.data()
data.set_ending_point(3000)
data.set_point_interval(10)
def run_sim1(n_A_GLUT, n_B_GLUT, n_SGLT1):
simulation.reset(True)
simulation.clear_results()
data.constants()["Apical_concentrations/glucose_m"] = 0.05
data.constants()["Cell_concentration/L_A"] = 5e-5
data.constants()["Cell_concentration/L_B"] = 5e-5
data.constants()["Blood_concentrations/v_B"] = 1e-17
data.constants()["Blood_concentrations/glucose_in"] = 0.005
data.constants()["Blood_concentrations/Q_in"] = 9e-18
data.constants()["A_GLUT2/n_GLUT"] = n_A_GLUT
data.constants()["phenomonological_constants/n_SGLT"] = n_SGLT1
data.constants()["GLUT2/n_GLUT"] = n_B_GLUT
simulation.run()
ds = simulation.results().data_store()
glucose_s = ds.voi_and_variables()["Blood_concentrations/glucose_s"].values()[-1]
glucose_i = ds.voi_and_variables()["Cell_concentration/glucose_i"].values()[-1]
J_GLUT = ds.voi_and_variables()["GLUT2/J_GLUT"].values()[-1]
J_Na_NaK = ds.voi_and_variables()["NaK_channel/J_Na_NaK"].values()[-1]
return (glucose_s, glucose_i, J_GLUT, J_Na_NaK)
#
if __name__ == '__main__':
#
n_A_GLUT = [0, 0.25*2e8, 0.5*2e8, 0.25*2e8, 1e5]
n_B_GLUT = [2e8, 0.75*2e8, 0.5*2e8, 0.75*2e8, 2e8]
n_SGLT1 = [4e7, 6e7, 4e7, 8e7, 8e7]
y_label = ["Blood Glucose", "Cell Glucose", "GLUT2 Flux"]
plt.figure(figsize=(15,10))
plt.subplot(1,3,1)
Gl_b_results = np.empty(len(n_A_GLUT))
for i in range(len(n_A_GLUT)):
Gl_b = run_sim1(n_A_GLUT[i], n_B_GLUT[i], n_SGLT1[i])[0]
Gl_b_results[i] = Gl_b
print(Gl_b_results)
width = 0.5 # the width of the bars
labels1 = ["A", "B", "C", "D", "E"]
plt.bar(labels1, Gl_b_results, width, color='r')
plt.ylim(0, 0.014)
plt.yticks(np.arange(0, 0.0121, 0.002))
plt.ylabel(y_label[0], fontsize= 12)
plt.title('A', fontsize= 14)
plt.subplot(1, 3, 2)
Gl_i_results = np.empty(len(n_A_GLUT))
for i in range(len(n_A_GLUT)):
Gl_i = run_sim1(n_A_GLUT[i], n_B_GLUT[i], n_SGLT1[i])[1]
Gl_i_results[i] = Gl_i
width = 0.5 # the width of the bars
plt.bar(labels1, Gl_i_results, width, color='b')
plt.ylim(0, 0.063)
plt.yticks(np.arange(0, 0.061, 0.02))
plt.ylabel(y_label[1], fontsize=12)
plt.title('B', fontsize=14)
plt.subplot(1, 3, 3)
J_GLUT_results = np.empty(len(n_A_GLUT))
for i in range(len(n_A_GLUT)):
J_GLUT = run_sim1(n_A_GLUT[i], n_B_GLUT[i], n_SGLT1[i])[2]
J_GLUT_results[i] = J_GLUT
width = 0.5 # the width of the bars
plt.bar(labels1, J_GLUT_results, width, color='g')
plt.ylim(0, 10e-11)
plt.yticks(np.arange(0, 8.1e-11, 2e-11))
plt.ylabel(y_label[2], fontsize=12)
plt.title('C', fontsize=14)
plt.savefig('Figure06.png')
plt.show()
