def vB6f(self, Pox,C,H):
""" calculates reaction rate of cytb6f """
ph = pH(H)
Keq = self.Keq_cytb6f(ph)
v = max(self.par.kCytb6f * ((self.par.PQtot - Pox) * C**2 - (Pox * (self.par.PCtot - C)**2)/Keq), -self.par.kCytb6f)
return v
def plotph(self, r=None):
""" plot lumenal pH """
if r == None:
r = range(len(self.results))
for i in r:
plt.plot(self.results[i]['t'],pH(self.results[i]['y'][:,5]))
def plotph(self, r=None):
"""
:param r: range of steps of simulation for which results we are interested in
:return: plot of lumenal ph
"""
if r is None:
r = range(len(self.results))
T = self.getT()
H = self.getVar(1)
plt.plot(T, misc.pH(H), 'g')
plt.title('Lumenal ph')
def plotph(self, r=None):
"""
:param r: range of steps of simulation for which results we are interested in
:return: plot of lumenal ph
"""
if r is None:
r = range(len(self.results))
T = self.getT()
H = self.getVar(1)
plt.plot(T, misc.pH(H), 'g')
plt.title('Lumenal ph')
def vPQox(self, Pred, Hlf, PFD):
"""
:param Pred: reduced PQ (PQH2)
:param ph: lumen pH (free protons)
:param PFD: light dependency implicit in b6f, because we do not have PSI in model
:return: rate of oxidation of the PQ pool through cytochrome and PTOX
"""
ph = pH(Hlf)
Keq = self.Keq_cytb6f(ph)
k = self.par.kCytb6f * PFD
kf = k * Keq / (Keq + 1)
kr = k / (Keq + 1)
kPTOX = self.par.kPTOX * self.par.xO2 # PTOX reaction added
v = (kf + kPTOX) * Pred - kr * (self.par.PQtot - Pred)
return v
def vPQox(self, Pred, Hlf, PFD):
"""
:param Pred: reduced PQ (PQH2)
:param ph: lumen pH (free protons)
:param PFD: light dependency implicit in b6f, because we do not have PSI in model
:return: rate of oxidation of the PQ pool through cytochrome and PTOX
"""
ph = pH(Hlf)
Keq = self.Keq_cytb6f(ph)
k = self.par.kCytb6f * PFD
kf = k * Keq/(Keq+1)
kr = k/(Keq+1)
kPTOX = self.par.kPTOX * self.par.xO2 # PTOX reaction added
v = (kf + kPTOX) * Pred - kr * (self.par.PQtot - Pred)
return v
def vATPsynthase(self, A, Hlf, Eact):
ph = pH(Hlf)
ADP = self.par.APtot - A
v = Eact * self.par.kATPsynth * (ADP - A / self.Keq_ATP(ph))
return v
def fig5(self):
"""
:return: figure visualizing pH dependance of quencher and its components.
Upper plot: phase plane trajectories
Bottom plots: pH and quencher components for 3 light intensities
"""
color = list(self.col[i] for i in [1,3,5]) # select 3 distinct colours
mark = ['x', 'o', 's']
v = visualizeexperiment.VisualizeExperiment('Arabidopsis', 100, 15)
# set up the figure
fig = plt.figure(figsize=(7, 10))
#make outer gridspec
outer_grid = gridspec.GridSpec(2, 1, hspace=0.2) # gridspec with two adjacent horizontal cellstwo rows
ax = plt.subplot(outer_grid[0])
ax.set_ylabel('lumen pH', fontsize=18)
ax.set_xlabel('quencher activity [Q]', fontsize=18)
ax.set_ylim(3, 8)
ax.set_xlim(0.05, 0.4)
ax.set_yticks([3,4,5,6,7,8])
ax.set_yticklabels(['',4,5,6,7,8], fontsize=14)
ax.set_xticks([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
ax.set_xticklabels(['','0.1','','0.2','','0.3','','0.4'], fontsize=14)
# add the steady state
m, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
ss = simulate.Sim(m)
scan = [10, 50, 100, 120, 150, 200, 220, 300, 320, 330, 340, 350, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500]
Y = ss.steadyStateLightScan(scan, y0)
ax.plot(m.quencher(Y[:,3], Y[:,4]), misc.pH(Y[:,1]), c='r', linestyle='-')
for i in[6, 8, 17]:
ax.plot(m.quencher(Y[i,3], Y[i,4]), misc.pH(Y[i,1]), 'o', linestyle='None',
markersize=15, mfc='r')
upper_cell = outer_grid[1]
inner_grid = gridspec.GridSpecFromSubplotSpec(2, 1, upper_cell, hspace=0.0)
# From here we can plot using inner_grid's SubplotSpecs
ax2 = plt.subplot(inner_grid[0, 0])
# add the three points for the calibrated light intensity
PFDs = [cpfd.cpfd('Arabidopsis', 100), cpfd.cpfd('Arabidopsis', 300), cpfd.cpfd('Arabidopsis', 900)]
time = [0, 30, 930, 1830, 2130]
nsteps = [31, 901, 901, 301] # nr of steps for each integration phase
v.setGraphics(time, 9, 1)
ax2.set_ylabel('lumen pH', fontsize=14)
ax2.set_ylim(2, 9)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(True)
ax2.set_yticks([2,3,4,5,6,7,8])
ax2.set_yticklabels(['',3,4,5,6,7,8], fontsize=14)
ax3 = plt.subplot(inner_grid[1, 0])
ax3.set_ylabel('Q components', fontsize=16)
ax3.set_xlabel('time [min]', fontsize=18)
ax3.set_xlim([0,time[-1]])
ax3.set_xticks(time[1:])
ax3.set_xticklabels(['30', '14', '30', '35'], fontsize=14)
ax3.set_yticks([0, 0.2, 0.5, 0.8, 1.])
ax3.set_yticklabels([0, 0.2, 0.5, 0.8, ''], fontsize=14)
for i in range(len(PFDs)):
s = simulate.Sim(m)
s.clearResults()
PFD = PFDs[i]
light = [0, PFD, 0, PFD]
s.piecewiseConstant(light, time[1:], y0, nsteps)
res = npqResults.NPQResults(s)
ax.plot(m.quencher(res.getVar(3), res.getVar(4)), misc.pH(res.getVar(1)), c=color[i],
marker=mark[i], label=str(self.light_intens[i]))
ax2.plot(res.getT(), misc.pH(res.getVar(1)), c=color[i], label=str(self.light_intens[i]))
ax3.plot(res.getT(), 1-res.getVar(3), c=color[i], linestyle='--', label=str(self.light_intens[i]))
ax3.plot(res.getT(), 1-res.getVar(4), c=color[i], linestyle='-', label=str(self.light_intens[i]))
handles, labels = ax.get_legend_handles_labels()
leg1 = ax.legend(handles, labels,
bbox_to_anchor=(0., 1.02, 1., .102), loc=10,
ncol=1,
borderaxespad=0.1)
if leg1:
leg1.draggable()
handles, labels = ax3.get_legend_handles_labels()
leg = ax3.legend([handles[0], handles[2], handles[4], handles[1], handles[3], handles[5]],
[labels[0], labels[2], labels[4], labels[1], labels[3], labels[5]],
bbox_to_anchor=(0., 1.02, 1., .102), loc=10,
title ='PsbS$^P$ Zx',
ncol=2,
borderaxespad=0.1)
if leg:
leg.draggable()
self.save_plot(fig, 'Fig5')
for i in range(len(PFDs)):
m = npqModel.NPQModel(p, 2)
s = simulate.Sim(m)
s.clearResults()
l = lightProtocol.LightProtocol({'prot':'const', 'PFD':PFDs[i]})
s.timeCourse(l, T, y0)
res = npqResults.NPQResults(s)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res.fluo()
Xref[i] = [m.quencher(res.getVar(3), res.getVar(4))[-1], misc.pH(res.getVar(1))[-1], res.getVar(0)[-1], F[-1]]
# change the value of gamma parameter by 1%
perturb = [1.01, 0.99]
# collect new calculated values for perturbated parameters for different light intensities
Xgl = np.zeros([len(Pref), 2, len(PFDs), 4])
Rgl = np.zeros([len(Pref), len(PFDs), 4])
for g in range(len(Pref)):
p = parameters.NPQmodelParameterSet()
m = npqModel.NPQModel(p, 2)
for pert in range(len(perturb)):
if g==0:
m.par.gamma0 = Pref[g] * perturb[pert]
elif g==1:
def vATPsynthase(self, A, Hlf):
ph = pH(Hlf)
ADP = self.par.APtot - A
v = self.par.kATPsynth * (ADP - A / self.Keq_ATP(ph))
return v
def fig5(self):
"""
:return: figure visualizing pH dependance of quencher and its components.
Upper plot: phase plane trajectories
Bottom plots: pH and quencher components for 3 light intensities
"""
color = list(self.col[i]
for i in [1, 3, 5]) # select 3 distinct colours
mark = ['x', 'o', 's']
v = visualizeexperiment.VisualizeExperiment('Arabidopsis', 100, 15)
# set up the figure
fig = plt.figure(figsize=(7, 10))
#make outer gridspec
outer_grid = gridspec.GridSpec(
2, 1,
hspace=0.2) # gridspec with two adjacent horizontal cellstwo rows
ax = plt.subplot(outer_grid[0])
ax.set_ylabel('lumen pH', fontsize=18)
ax.set_xlabel('quencher activity [Q]', fontsize=18)
ax.set_ylim(3, 8)
ax.set_xlim(0.05, 0.4)
ax.set_yticks([3, 4, 5, 6, 7, 8])
ax.set_yticklabels(['', 4, 5, 6, 7, 8], fontsize=14)
ax.set_xticks([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
ax.set_xticklabels(['', '0.1', '', '0.2', '', '0.3', '', '0.4'],
fontsize=14)
# add the steady state
m, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
ss = simulate.Sim(m)
scan = [
10, 50, 100, 120, 150, 200, 220, 300, 320, 330, 340, 350, 400, 500,
600, 700, 800, 900, 1000, 1250, 1500
]
Y = ss.steadyStateLightScan(scan, y0)
ax.plot(m.quencher(Y[:, 3], Y[:, 4]),
misc.pH(Y[:, 1]),
c='r',
linestyle='-')
for i in [6, 8, 17]:
ax.plot(m.quencher(Y[i, 3], Y[i, 4]),
misc.pH(Y[i, 1]),
'o',
linestyle='None',
markersize=15,
mfc='r')
upper_cell = outer_grid[1]
inner_grid = gridspec.GridSpecFromSubplotSpec(2,
1,
upper_cell,
hspace=0.0)
# From here we can plot using inner_grid's SubplotSpecs
ax2 = plt.subplot(inner_grid[0, 0])
# add the three points for the calibrated light intensity
PFDs = [
cpfd.cpfd('Arabidopsis', 100),
cpfd.cpfd('Arabidopsis', 300),
cpfd.cpfd('Arabidopsis', 900)
]
time = [0, 30, 930, 1830, 2130]
nsteps = [31, 901, 901, 301] # nr of steps for each integration phase
v.setGraphics(time, 9, 1)
ax2.set_ylabel('lumen pH', fontsize=14)
ax2.set_ylim(2, 9)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(True)
ax2.set_yticks([2, 3, 4, 5, 6, 7, 8])
ax2.set_yticklabels(['', 3, 4, 5, 6, 7, 8], fontsize=14)
ax3 = plt.subplot(inner_grid[1, 0])
ax3.set_ylabel('Q components', fontsize=16)
ax3.set_xlabel('time [min]', fontsize=18)
ax3.set_xlim([0, time[-1]])
ax3.set_xticks(time[1:])
ax3.set_xticklabels(['30', '14', '30', '35'], fontsize=14)
ax3.set_yticks([0, 0.2, 0.5, 0.8, 1.])
ax3.set_yticklabels([0, 0.2, 0.5, 0.8, ''], fontsize=14)
for i in range(len(PFDs)):
s = simulate.Sim(m)
s.clearResults()
PFD = PFDs[i]
light = [0, PFD, 0, PFD]
s.piecewiseConstant(light, time[1:], y0, nsteps)
res = npqResults.NPQResults(s)
ax.plot(m.quencher(res.getVar(3), res.getVar(4)),
misc.pH(res.getVar(1)),
c=color[i],
marker=mark[i],
label=str(self.light_intens[i]))
ax2.plot(res.getT(),
misc.pH(res.getVar(1)),
c=color[i],
label=str(self.light_intens[i]))
ax3.plot(res.getT(),
1 - res.getVar(3),
c=color[i],
linestyle='--',
label=str(self.light_intens[i]))
ax3.plot(res.getT(),
1 - res.getVar(4),
c=color[i],
linestyle='-',
label=str(self.light_intens[i]))
handles, labels = ax.get_legend_handles_labels()
leg1 = ax.legend(handles,
labels,
bbox_to_anchor=(0., 1.02, 1., .102),
loc=10,
ncol=1,
borderaxespad=0.1)
if leg1:
leg1.draggable()
handles, labels = ax3.get_legend_handles_labels()
leg = ax3.legend([
handles[0], handles[2], handles[4], handles[1], handles[3],
handles[5]
], [labels[0], labels[2], labels[4], labels[1], labels[3], labels[5]],
bbox_to_anchor=(0., 1.02, 1., .102),
loc=10,
title='PsbS$^P$ Zx',
ncol=2,
borderaxespad=0.1)
if leg:
leg.draggable()
self.save_plot(fig, 'Fig5')
m = npqModel.NPQModel(p, 2)
s = simulate.Sim(m)
s.clearResults()
l = lightProtocol.LightProtocol({'prot': 'const', 'PFD': PFDs[i]})
s.timeCourse(l, T, y0)
res = npqResults.NPQResults(s)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res.fluo()
Xref[i] = [
m.quencher(res.getVar(3), res.getVar(4))[-1],
misc.pH(res.getVar(1))[-1],
res.getVar(0)[-1], F[-1]
]
# change the value of gamma parameter by 1%
perturb = [1.01, 0.99]
# collect new calculated values for perturbated parameters for different light intensities
Xgl = np.zeros([len(Pref), 2, len(PFDs), 4])
Rgl = np.zeros([len(Pref), len(PFDs), 4])
for g in range(len(Pref)):
p = parameters.NPQmodelParameterSet()
m = npqModel.NPQModel(p, 2)
for pert in range(len(perturb)):
if g == 0: