def make_plot():
run1, run2, t = util.bload( 'Bb-run.bin' )
# take every 10th point
step = 20
run1 = skip(run1, step=step)
run2 = skip(run2, step=step)
t = skip(t, step=step)
nodes = "EC PIC C PH IL12I IL12II".split()
subplot(121)
# drawing these first so that the symbols are under the other ones
p1 = plot(t, run2['EC'], 'r^-', ms=7 )
p2 = plot(t, run2['PIC'], 'r^-', ms=7 )
p7 = plot(t, run1['EC'], 'b.-', ms=5 )
p8 = plot(t, run1['PIC'], 'b--', ms=5 )
xlabel( 'Time' )
ylabel( 'Concentration' )
title ( 'Innate Immune Response' )
legend( [p1, p2, p7, p8], 'DEL-EC DEL-PIC WT-EC WT-PIC'.split(), loc='best')
subplot(122)
p3 = plot(t, run2['C'], 'r^-', ms=7 )
p4 = plot(t, run2['PH'], 'ro-', ms=7 )
p5 = plot(t, run2['IL12I'], 'r.-', ms=7 )
p6 = plot(t, run2['IL12II'], 'rs-', ms=7 )
p9 = plot(t, run1['C'], 'bo-', ms=5 )
p10 = plot(t, run1['PH'] , 'bD-', ms=5 )
p11 = plot(t, run1['IL12I'], 'b.-', ms=5 )
p12 = plot(t, run1['IL12II'], 'b^-', ms=5 )
xlabel( 'Time' )
ylabel( 'Concentration' )
title ( 'Adaptive Immune Response' )
legend( [p3, p4, p5, p6, p9, p10, p11, p12], 'DEL-C DEL-PH DEL-IL12I DEL-IL12II WT-C WT-PH WT-IL12I WT-IL12II'.split(), loc='best')
def make_plot():
# contains averaged node information based on 1000 runs
data = util.bload( 'LGL-run.bin' )
# each of these is a dictionary keyed by nodes
run1, run2, run3, run4 = data
# applies smoothing to all values
for run in (run1, run2, run3, run4):
for key, values in run.items():
run[key] = smooth( values, w=10 )
#
# Plotting Apoptosis
#
subplot(121)
apop1, apop2, apop3, apop4 = run1['Apoptosis'], run2['Apoptosis'],run3['Apoptosis'],run4['Apoptosis']
ps = [ plot( apop1, 'bo-' ), plot( apop2, 'ro-' ),plot(apop3,'b^-'),plot(apop4,'r^-') ]
legend( ps, ['Normal-Apop', 'MCL1-over-Apop','sFas-over-Apop','LGL-like-Apop' ], loc='best' )
title( ' Changes in Apoptosis' )
xlabel( 'Time Steps' )
ylabel( 'Percent (%)' )
ylim( (-0.1, 1.1) )
#
# Plotting FasL and Ras
#
subplot(122)
fasL1, fasL2 = run1['FasL'], run4['FasL']
ras1, ras2 = run1['Ras'], run4['Ras']
ps = [ plot( fasL1, 'bo-' ), plot( fasL2, 'ro-' ), plot( ras1, 'b^-' ), plot( ras2, 'r^-' ) ]
legend( ps, 'Normal-FasL LGL-like-FasL Normal-Ras LGL-like-Ras'.split() , loc='lower left' )
title( ' Changes in FasL and Ras' )
xlabel( 'Time Steps' )
def make_plot():
# contains averaged node information based on 1000 runs
data = util.bload('gene.bin')
# each of these is a dictionary keyed by nodes
run1, run2, run3, run4 = data
# applies smoothing to all values
for run in (run1, run2, run3, run4):
for key, values in run.items():
run[key] = smooth(values, w=10)
#
# Plotting Migration (ON)
#
fig = plt.figure(figsize=(4.5, 2.5), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2 = run1['Migration'], run2['Migration']
mig3, mig4 = run3['Migration'], run4['Migration']
ps = [
plot(mig1, 'go-', markersize=3, linewidth=0.2),
plot(mig2, 'ro-', markersize=3, linewidth=0.2),
plot(mig3, 'g^-', markersize=3, linewidth=0.2),
plot(mig4, 'r^-', markersize=3, linewidth=0.2)
]
legend(['OE RHEB', 'OE DISC1', 'KO RHEB', 'KO DISC1'],
loc='upper center',
bbox_to_anchor=(0.5, -0.15),
ncol=4,
fontsize=7)
ylim((-0.1, 1.1))
xlim((-0.1, 140))
title(
' Effect of perturbation of genes on migration \n Functional module 8, Simulation 2',
fontsize=10)
xlabel('Time Steps', fontsize=9)
ylabel('Percent (%)', fontsize=9)
plott.tick_params(axis='both', which='major', labelsize=8)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
savefig('RHEB_gene_Sim2_1-140.svg', transparent=True, bbox_inches='tight')
savefig('RHEB_gene_Sim2_1-140.png', transparent=True, bbox_inches='tight')
savefig('RHEB_gene_Sim2_1-140.pdf', transparent=True, bbox_inches='tight')
# Plotting t=0 to t=20
fig = figure(figsize=(2.25, 1.25), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2 = run1['Migration'], run2['Migration']
mig3, mig4 = run3['Migration'], run4['Migration']
ps = [
plot(mig1, 'go-', markersize=2, linewidth=0.1),
plot(mig2, 'ro-', markersize=2, linewidth=0.1),
plot(mig3, 'g^-', markersize=2, linewidth=0.1),
plot(mig4, 'r^-', markersize=2, linewidth=0.1)
]
ylim((-0.1, 1.1))
xlim((-0.1, 20))
plott.tick_params(axis='both', which='major', labelsize=6)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
plott.spines['right'].set_visible(False)
plott.spines['top'].set_visible(False)
savefig('RHEB_gene_Sim2_1-20.svg', transparent=True, bbox_inches='tight')
savefig('RHEB_gene_Sim2_1-20.png', transparent=True, bbox_inches='tight')
savefig('RHEB_gene_Sim2_1-20.pdf', transparent=True, bbox_inches='tight')
def make_plot():
# contains averaged node information based on 1000 runs
data = util.bload('TF1.bin')
# each of these is a dictionary keyed by nodes
run1, run2, run3, run4, run5, run6, run7, run8, run9, run10 = data
# applies smoothing to all values
for run in (run1, run2, run3, run4, run5, run6, run7, run8, run9, run10):
for key, values in run.items():
run[key] = smooth(values, w=10)
#
# Plotting Migration (ON)
#
fig = plt.figure(figsize=(4.5, 2.5), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2, mig3, mig4, mig5 = run1['Migration'], run2['Migration'], run3[
'Migration'], run4['Migration'], run5['Migration']
mig6, mig7, mig8, mig9, mig10 = run6['Migration'], run7['Migration'], run8[
'Migration'], run9['Migration'], run10['Migration']
ps = [
plot(mig1, 'go-', markersize=3, linewidth=0.2),
plot(mig2, 'ro-', markersize=3, linewidth=0.2),
plot(mig3, 'bo-', markersize=3, linewidth=0.2),
plot(mig4, 'yo-', markersize=3, linewidth=0.2),
plot(mig5, 'co-', markersize=3, linewidth=0.2),
plot(mig6, 'g^-', markersize=3, linewidth=0.2),
plot(mig7, 'r^-', markersize=3, linewidth=0.2),
plot(mig8, 'b^-', markersize=3, linewidth=0.2),
plot(mig9, 'y^-', markersize=3, linewidth=0.2),
plot(mig10, 'c^-', markersize=3, linewidth=0.2)
]
legend([
'OE CREB1', 'OE CCND1', 'OE KLF4', 'OE SMAD2', 'OE SMAD3', 'KO CREB1',
'KO CCND1', 'KO KLF4', 'KO SMAD2', 'KO SMAD3'
],
loc='upper center',
bbox_to_anchor=(0.5, -0.15),
ncol=4,
fontsize=7)
title(
' Effect of perturbation of TFs on migration \n Functional module 3, Simulation 4',
fontsize=10)
xlabel('Time Steps', fontsize=9)
ylabel('Percent (%)', fontsize=9)
ylim((-0.1, 1.1))
xlim((-0.1, 140))
plott.tick_params(axis='both', which='major', labelsize=8)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
savefig('CDK5_TF_Sim4_1-140_plot1.svg',
transparent=True,
bbox_inches='tight')
savefig('CDK5_TF_Sim4_1-140_plot1.png',
transparent=True,
bbox_inches='tight')
savefig('CDK5_TF_Sim4_1-140_plot1.pdf',
transparent=True,
bbox_inches='tight')
#
fig = figure(figsize=(2.25, 1.25), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2, mig3, mig4, mig5 = run1['Migration'], run2['Migration'], run3[
'Migration'], run4['Migration'], run5['Migration']
mig6, mig7, mig8, mig9, mig10 = run6['Migration'], run7['Migration'], run8[
'Migration'], run9['Migration'], run10['Migration']
ps = [
plot(mig1, 'go-', markersize=2, linewidth=0.1),
plot(mig2, 'ro-', markersize=2, linewidth=0.1),
plot(mig3, 'bo-', markersize=2, linewidth=0.1),
plot(mig4, 'yo-', markersize=2, linewidth=0.1),
plot(mig5, 'co-', markersize=2, linewidth=0.1),
plot(mig6, 'g^-', markersize=2, linewidth=0.1),
plot(mig7, 'r^-', markersize=2, linewidth=0.1),
plot(mig8, 'b^-', markersize=2, linewidth=0.1),
plot(mig9, 'y^-', markersize=2, linewidth=0.1),
plot(mig10, 'c^-', markersize=2, linewidth=0.1)
]
ylim((-0.1, 1.1))
xlim((-0.1, 20))
plott.tick_params(axis='both', which='major', labelsize=6)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
plott.spines['right'].set_visible(False)
plott.spines['top'].set_visible(False)
savefig('CDK5_TF_Sim4_1-20_plot1.svg',
transparent=True,
bbox_inches='tight')
savefig('CDK5_TF_Sim4_1-20_plot1.png',
transparent=True,
bbox_inches='tight')
savefig('CDK5_TF_Sim4_1-20_plot1.pdf',
transparent=True,
bbox_inches='tight')
def make_plot():
# contains averaged node information based on 1000 runs
data = util.bload('miRNA1.bin')
# each of these is a dictionary keyed by nodes
run1, run2, run3, run4, run5, run6, run7, run8, run9, run10, run11, run12 = data
# applies smoothing to all values
for run in (run1, run2, run3, run4, run5, run6, run7, run8, run9, run10,
run11, run12):
for key, values in run.items():
run[key] = smooth(values, w=10)
#
# Plotting Migration (ON)
#
fig = figure(figsize=(4.5, 2.5), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2, mig3, mig4, mig5, mig6 = run1['Migration'], run2[
'Migration'], run3['Migration'], run4['Migration'], run5[
'Migration'], run6['Migration']
mig7, mig8, mig9, mig10, mig11, mig12 = run7['Migration'], run8[
'Migration'], run9['Migration'], run10['Migration'], run11[
'Migration'], run12['Migration']
ps = [ plot( mig1, 'go-',markersize=3, linewidth=0.2 ), plot( mig2, 'ro-', markersize=3, linewidth=0.2 ),plot( mig3,'bo-', markersize=3, linewidth=0.2),\
plot( mig4, 'yo-', markersize=3, linewidth=0.2 ), plot( mig5, 'co-', markersize=3, linewidth=0.2 ), plot( mig6, color='orange', marker='o', linestyle='-', markersize=3, linewidth=0.2), \
plot( mig7, 'g^-', markersize=3, linewidth=0.2 ), plot( mig8, 'r^-', markersize=3, linewidth=0.2 ), plot( mig9, 'b^-', markersize=3, linewidth=0.2 ), plot( mig10, 'y^-', markersize=3, linewidth=0.2 ), \
plot( mig11, 'c^-' , markersize=3, linewidth=0.2), plot( mig12, color='orange', marker='^', linestyle='-', markersize=3, linewidth=0.2)]
ylim((-0.1, 1.1))
xlim((-0.1, 140))
legend([
'OE miR320a', 'OE miR223', 'OE miR155', 'OE miR106a', 'OE miR17',
'OE miR130a', 'KO miR320a', 'KO miR223', 'KO miR155', 'KO miR106a',
'KO miR17', 'KO miR130a'
],
loc='upper center',
bbox_to_anchor=(0.5, -0.18),
ncol=4,
fontsize=7)
title(
' Effect of perturbation of miRNAs on migration \n Functional module 7, Simulation 2',
fontsize=10)
xlabel('Time Steps', fontsize=9)
ylabel('Percent (%)', fontsize=9)
plott.tick_params(axis='both', which='major', labelsize=8)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
savefig('SOX10_miRNA_Sim2_1-140-plot1.svg',
transparent=True,
bbox_inches='tight')
savefig('SOX10_miRNA_Sim2_1-140-plot1.png',
transparent=True,
bbox_inches='tight')
savefig('SOX10_miRNA_Sim2_1-140-plot1.pdf',
transparent=True,
bbox_inches='tight')
# Plotting Migration (OFF)
#
fig = figure(figsize=(2.25, 1.25), facecolor='w', edgecolor='k')
plott = fig.add_subplot(111)
mig1, mig2, mig3, mig4, mig5, mig6 = run1['Migration'], run2[
'Migration'], run3['Migration'], run4['Migration'], run5[
'Migration'], run6['Migration']
mig7, mig8, mig9, mig10, mig11, mig12 = run7['Migration'], run8[
'Migration'], run9['Migration'], run10['Migration'], run11[
'Migration'], run12['Migration']
ps = [ plot( mig1, 'go-', markersize=2, linewidth=0.1 ), plot( mig2, 'ro-', markersize=2, linewidth=0.1 ),plot( mig3,'bo-', markersize=2, linewidth=0.1),plot( mig4, 'yo-', markersize=2, linewidth=0.1 ),\
plot( mig5, 'co-', markersize=2, linewidth=0.1 ), plot( mig6, color='orange', marker='o', linestyle='-', markersize=2, linewidth=0.1 ), plot( mig7, 'g^-', markersize=2, linewidth=0.1 ), \
plot( mig8, 'r^-', markersize=2, linewidth=0.1 ), plot( mig9, 'b^-', markersize=2, linewidth=0.1 ), plot( mig10, 'y^-', markersize=2, linewidth=0.1 ), plot( mig11, 'c^-', markersize=2, linewidth=0.1 ), \
plot( mig12, color='orange', marker='^', linestyle='-', markersize=2, linewidth=0.1 )]
ylim((-0.1, 1.1))
xlim((-0.1, 20))
plott.tick_params(axis='both', which='major', labelsize=6)
plott.yaxis.set_ticks_position('left')
plott.xaxis.set_ticks_position('bottom')
plott.spines['right'].set_visible(False)
plott.spines['top'].set_visible(False)
savefig('SOX10_miRNA_Sim2_1-20-plot1.svg',
transparent=True,
bbox_inches='tight')
savefig('SOX10_miRNA_Sim2_1-20-plot1.png',
transparent=True,
bbox_inches='tight')
savefig('SOX10_miRNA_Sim2_1-20-plot1.pdf',
transparent=True,
bbox_inches='tight')