fig1.show()
fig2 = plt.figure(figsize=(14, 6))
fig2.suptitle("Valdiation")
ax_enz = fig2.add_subplot(1, 2, 1, xscale="log", yscale="log")
ax_enz.set_title("eCF(1SP) Enzyme concentrations")
ax_met = fig2.add_subplot(1, 2, 2, xscale="log", yscale="log")
ax_met.set_title("eCF(1SP) Metabolite concentrations")
model.ValidateMetaboliteConcentrations(lnC_ECM, ax_met)
model.ValidateEnzymeConcentrations(lnC_ECM, ax_enz)
fig2.show()
html.write("<p>\n")
html.write("<b>Experiment name:</b> %s</br>\n" % exp_name)
html.embed_matplotlib_figure(fig1)
html.write("</p><p>\n")
html.embed_matplotlib_figure(fig2)
html.write("</p><p>\n")
model.WriteHtmlTables(lnC_ECM, html)
html.write("</p>\n")
html.close()
# make plots also in linear scale just for fun
fig3 = plt.figure(figsize=(14, 6))
fig3.suptitle("Valdiation")
ax_enz = fig3.add_subplot(1, 2, 1, xscale="linear", yscale="linear")
ax_enz.set_title("eCF3(1SP) Enzyme concentrations")
ax_met = fig3.add_subplot(1, 2, 2, xscale="linear", yscale="linear")
def main(core=True):
main_html = HtmlWriter('res/optknock.html')
main_html.write('<title>OptKnock</title>')
main_html.write('<h1>OptKnock</h1>\n')
if core:
model = init_wt_model('core', {}, BM_lower_bound=0.1)
knockin_reactions(model, 'EDD,EDA', 0, 1000)
set_exchange_bounds(model, 'g6p', lower_bound=-10)
main_html.write('<ul>\n')
main_html.write('<li>Model used: E.coli core</li>\n')
main_html.write(
'<li>Carbon source: glucose-6P, v <= 10 [mmol/g(DW) h]</li>\n')
main_html.write(
'<li>Biomass yield lower bound: v >= 0.1 [mmol/g(DW) h]</li>\n')
else:
model = init_wt_model('full', {'rib_D': -10}, BM_lower_bound=0.1)
main_html.write('<ul>\n')
main_html.write('<li>Model used: iJO1366 (E. coli full model)</li>\n')
main_html.write(
'<li>Carbon source: D-ribose, v <= 10 [mmol/g(DW) h]</li>\n')
main_html.write(
'<li>Biomass yield lower bound: v >= 0.1 [mmol/g(DW) h]</li>\n')
knockin_reactions(model, 'PRK,RBC', 0, 1000)
main_html.write('<li>Added reactions: phosphoribulokinase, RuBisCo</li>\n')
optknock_reaction = 'RBC'
#knockin_reactions(model, 'DXS', 0, 1000)
#main_html.write('<li>Added reactions: deoxyribose synthase</li>\n')
#optknock_reaction = 'DXS'
main_html.write('</ul>\n')
print "Running standard FBA..."
ok = OptKnock(model)
ok.prepare_FBA_primal()
ok.prob.writeLP('res/fba_primal.lp')
ok.solve()
ok.print_primal_results(short=True)
print '-' * 50
print "Running dual FBA..."
ok = OptKnock(model)
ok.prepare_FBA_dual()
ok.prob.writeLP('res/fba_dual.lp')
ok.solve()
ok.print_dual_results(short=True)
print '-' * 50
if False:
print "Running OptSlope..."
ok = OptKnock(model)
ok.prepare_optslope(optknock_reaction, num_deletions=3)
ok.write_linear_problem('res/optslope.lp')
solution = ok.solve()
if solution.status is not None:
ok.print_optknock_results(short=True)
ok_model = ok.get_optknock_model()
#fig, ax = plt.subplots(1)
#plot_multi_PPP([model, ok_model],
# ['Wild-Type', r'OptKnock'],
# optknock_reaction, ax)
#ax.set_title(title)
#fig.savefig('res/OS_PPP.pdf')
print '-' * 50
print "Running OptKnock for maximizing flux in %s..." % optknock_reaction
ok = OptKnock(model)
ok.prepare_optknock(optknock_reaction, num_deletions=1)
ok.write_linear_problem('res/optknock.lp')
solution = ok.solve()
if solution.status is not None:
ok.print_optknock_results(short=True)
knockouts = ok.get_optknock_knockouts()
ko_model = clone_model(model)
knockout_reactions(ko_model, knockouts)
main_html.write('<h2>Knockouts: %s</h2>\n' % knockouts)
# draw the PPP as embedded SVG
fig, ax = plt.subplots(1, figsize=(6, 6))
wt_PPP, wt_slope = get_PPP(model, optknock_reaction)
ax.fill_between(wt_PPP[:, 0].flat,
wt_PPP[:, 1].flat,
wt_PPP[:, 2].flat,
facecolor='#E0E0E0',
linewidth=0)
ko_PPP, slope = get_PPP(ko_model, optknock_reaction)
if slope is None:
ko_text = 'Not feasible at any Rubisco flux'
ko_color = '#FF9073'
else:
slope = np.round(slope, 1)
ko_text = ('Slope = %g' % slope)
if slope > 0:
ko_color = '#00B64F'
else:
ko_color = '#FF7060'
ax.fill_between(ko_PPP[:, 0].flat,
ko_PPP[:, 1].flat,
ko_PPP[:, 2].flat,
facecolor=ko_color,
linewidth=1)
main_html.embed_matplotlib_figure(fig, width=400, height=400)
# draw the flux map as embedded SVG
ok.draw_svg(main_html)
# write the flux summary for the knockout model as HTML
ok.model_summary(main_html)
def main():
main_html = HtmlWriter('res/fba.html')
main_html.write('<h1>Flux Balance Analysis</h1>\n')
carbon_sources = {}
BM_lower_bound = 0.01
ko_reactions = ''
ki_reactions = ''
PPP_reaction = ''
# full model carbon sources: glc, fru, xyl__D, succ, ac, rib__D, pyr
#########################################
# Core model for testing glycolysis KOs #
#########################################
#model = init_wt_model('core', {'ac' : -10}); ko_reactions = 'PGM'; ki_reactions = 'RED';
#model = init_wt_model('core', {'glc' : -10}); ko_reactions = 'PFK'; ki_reactions = 'RED';
#######################################
# Full model Rubisco optimization KOs #
#######################################
#model = init_wt_model('full', {'ac' : -10}); ko_reactions = 'PGM,TRSARr,HPYRRx,HPYRRy'; ki_reactions = 'RED';
#model = init_wt_model('full', {'rib_D' : -10}); ko_reactions = 'TKT1'; ki_reactions = 'RBC,PRK'; PPP_reaction = 'RBC';
model = init_wt_model('full', {'xyl_D': -10})
ko_reactions = 'RPI'
ki_reactions = 'RBC,PRK'
PPP_reaction = 'RBC'
#model = init_wt_model('full', {'xyl_D' : -10}); ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA'; ki_reactions = 'RBC,PRK';
#model = init_wt_model('full', {'fru' : -10, 'rib_D' : -10}); ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA,TKT1,TKT2'; ki_reactions = 'PKT';
###############################
# Shikimate generating strain #
###############################
#model = init_wt_model('full', {'fru' : -10});
#ko_reactions = 'G6PDH2r,TALA'; ki_reactions = 'EX_3dhsk_c'; PPP_reaction = 'EX_3dhsk_c';
#ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA'; ki_reactions = 'PKT'; PPP_reaction = 'PKT';
##########################################################
# Testing no growth when electrons are provided directly #
##########################################################
#model = init_wt_model('full', {}); ki_reactions = 'RED';
#ko_reactions = "POR5,MCITL2";
#ko_reactions = "POR5,FTHFLi,GART,RPI";
models = {'WT': model}
if ko_reactions:
for k in models.keys():
m = deepcopy(models[k])
knockout_reactions(m, ko_reactions)
models[k + ' -%s' % ko_reactions] = m
if ki_reactions:
for k in models.keys():
m = deepcopy(models[k])
knockin_reactions(m, ki_reactions)
models[k + ' +%s' % ki_reactions] = m
# Run the optimization for the objective reaction and medium composition
# set in the file.
main_html.write('<table border="1">\n')
main_html.write(
'<tr><td><b>Model Name</b></td><td><b>Growth Yield</b></td></tr>\n')
growths = {}
for name, model in sorted(models.iteritems()):
print "solving %50s model" % name,
ok = OptKnock(model)
ok.prepare_FBA_primal()
ok.solve()
growths[name] = ok.get_objective_value()
if growths[name] is None:
main_html.write('<tr><td>%s</td><td>infeasible</td></tr>\n' % name)
else:
print ': f = %.3g' % growths[name]
main_html.write('<tr><td>')
html = main_html.branch(name)
main_html.write('</td><td>%.3g</td></tr>\n' % growths[name])
html.write('<h1>Model name: %s</h1>\n' % name)
html.write('<h2>Growth Yield: %.3g</h2>\n' % growths[name])
ok.draw_svg(html)
ok.model_summary(html)
main_html.write('</table>\n')
if PPP_reaction:
print 'Calculating Phenotypic Phase Plane for phosphoketolase ...'
fig, ax = plt.subplots(1, figsize=(6, 6))
plot_multi_PPP(models, PPP_reaction, ax)
ax.set_title('Phenotypic Phase Plane')
main_html.embed_matplotlib_figure(fig, width=400, height=400)
def main():
main_html = HtmlWriter('res/fba.html')
main_html.write('<h1>Flux Balance Analysis</h1>\n')
carbon_sources = {}
BM_lower_bound = 0.01
ko_reactions = ''
ki_reactions = ''
PPP_reaction = ''
# full model carbon sources: glc, fru, xyl__D, succ, ac, rib__D, pyr
#########################################
# Core model for testing glycolysis KOs #
#########################################
#model = init_wt_model('core', {'ac' : -10}); ko_reactions = 'PGM'; ki_reactions = 'RED';
#model = init_wt_model('core', {'glc' : -10}); ko_reactions = 'PFK'; ki_reactions = 'RED';
#######################################
# Full model Rubisco optimization KOs #
#######################################
#model = init_wt_model('full', {'ac' : -10}); ko_reactions = 'PGM,TRSARr,HPYRRx,HPYRRy'; ki_reactions = 'RED';
#model = init_wt_model('full', {'rib_D' : -10}); ko_reactions = 'TKT1'; ki_reactions = 'RBC,PRK'; PPP_reaction = 'RBC';
model = init_wt_model('full', {'xyl_D' : -10}); ko_reactions = 'RPI'; ki_reactions = 'RBC,PRK'; PPP_reaction = 'RBC';
#model = init_wt_model('full', {'xyl_D' : -10}); ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA'; ki_reactions = 'RBC,PRK';
#model = init_wt_model('full', {'fru' : -10, 'rib_D' : -10}); ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA,TKT1,TKT2'; ki_reactions = 'PKT';
###############################
# Shikimate generating strain #
###############################
#model = init_wt_model('full', {'fru' : -10});
#ko_reactions = 'G6PDH2r,TALA'; ki_reactions = 'EX_3dhsk_c'; PPP_reaction = 'EX_3dhsk_c';
#ko_reactions = 'G6PDH2r,PFK,F6PA,FRUK,PFK_3,DRPA'; ki_reactions = 'PKT'; PPP_reaction = 'PKT';
##########################################################
# Testing no growth when electrons are provided directly #
##########################################################
#model = init_wt_model('full', {}); ki_reactions = 'RED';
#ko_reactions = "POR5,MCITL2";
#ko_reactions = "POR5,FTHFLi,GART,RPI";
models = {'WT' : model}
if ko_reactions:
for k in models.keys():
m = deepcopy(models[k])
knockout_reactions(m, ko_reactions)
models[k + ' -%s' % ko_reactions] = m
if ki_reactions:
for k in models.keys():
m = deepcopy(models[k])
knockin_reactions(m, ki_reactions)
models[k + ' +%s' % ki_reactions] = m
# Run the optimization for the objective reaction and medium composition
# set in the file.
main_html.write('<table border="1">\n')
main_html.write('<tr><td><b>Model Name</b></td><td><b>Growth Yield</b></td></tr>\n')
growths = {}
for name, model in sorted(models.iteritems()):
print "solving %50s model" % name,
ok = OptKnock(model)
ok.prepare_FBA_primal()
ok.solve()
growths[name] = ok.get_objective_value()
if growths[name] is None:
main_html.write('<tr><td>%s</td><td>infeasible</td></tr>\n' % name)
else:
print ': f = %.3g' % growths[name]
main_html.write('<tr><td>')
html = main_html.branch(name)
main_html.write('</td><td>%.3g</td></tr>\n' % growths[name])
html.write('<h1>Model name: %s</h1>\n' % name)
html.write('<h2>Growth Yield: %.3g</h2>\n' % growths[name])
ok.draw_svg(html)
ok.model_summary(html)
main_html.write('</table>\n')
if PPP_reaction:
print 'Calculating Phenotypic Phase Plane for phosphoketolase ...'
fig, ax = plt.subplots(1, figsize=(6,6))
plot_multi_PPP(models, PPP_reaction, ax)
ax.set_title('Phenotypic Phase Plane')
main_html.embed_matplotlib_figure(fig, width=400, height=400)
