def __init__(self, modules_file, experiment_name, max_module_size=6):
self.modules_file = modules_file
self.experiment_name = experiment_name
self.max_module_size = max_module_size
util._mkdir("../log")
self.logfile = open(
"../log/pathologic_" + self.experiment_name + ".log", "w")
util._mkdir("../results")
util._mkdir("../results/pathologic_" + self.experiment_name)
self.html_writer = HtmlWriter("../results/pathologic_" +
self.experiment_name + ".html")
self.html_writer.write(
"<h1>List of optimal and non-optimal pathways</h1>\n")
self.html_writer.write("<ul>\n")
self.line_counter = 0
self.pathfinder = None
def __init__(self, modules_file, experiment_name, max_module_size=6):
self.modules_file = modules_file
self.experiment_name = experiment_name
self.max_module_size = max_module_size
util._mkdir("../log")
self.logfile = open("../log/pathologic_" + self.experiment_name + ".log", "w")
util._mkdir("../results")
util._mkdir("../results/pathologic_" + self.experiment_name)
self.html_writer = HtmlWriter("../results/pathologic_" + self.experiment_name + ".html")
self.html_writer.write("<h1>List of optimal and non-optimal pathways</h1>\n")
self.html_writer.write("<ul>\n")
self.line_counter = 0
self.pathfinder = None
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():
Pentose_target_compounds = [
'D-Xylulose-5P', 'D-Ribulose-5P', 'D-Ribose-5P'
]
PP_compounds = \
['D-Xylulose-5P',\
'D-Ribulose-5P',\
'D-Ribose-5P',\
'D-Erythrose-4P',\
'D-Sedoheptulose-7P',\
'D-Fructose-6P',\
'D-Glyceraldehyde-3P']
Glycolysis_compounds = \
['Dihydroxyacetone-3P',\
'D-Glyceraldehyde-3P',\
'Bisphosphoglycerate',\
'3-Phosphoglycerate',\
'2-Phosphoglycerate',\
'Phosphoenolpyruvate',\
'Pyruvate']
TCA_compounds = \
['Oxaloacetate',\
'Citrate',\
'cis-Aconitate',\
'D-Isocitrate',\
'2-Ketoglutarate',\
#succinyl-CoA\
'Succinate',\
'Fumarate',\
'Malate',\
]
Biosynthese_compounds = PP_compounds + Glycolysis_compounds + TCA_compounds
pathway_list = []
# fast test
pathway_list.append(
("TEST", ['2-Phosphoglycerate'], ['Bisphosphoglycerate'], None))
# Optimality Modules:
pathway_list.append(
("Glucose to Fructose", ['D-glucose-6P'], ['D-fructose-6P'], None))
pathway_list.append(
("Fructose to Glucose", ['D-fructose-6P'], ['D-glucose-6P'], None))
pathway_list.append(("oxaloacetate+acetyl-CoA to citrate",
['oxaloacetate + acetyl-CoA'], ['citrate'], None))
pathway_list.append(
("cis-aconitate to succinate", ['cis-aconitate'], ['succinate'], None))
pathway_list.append(
("D-xylose to D-xylulose-5P", ['D-Xylose'], ['D-Xylulose-5P'], None))
pathway_list.append(("D-arabitol to D-xylulose-5P", ['D-Arabitol'],
['D-Xylulose-5P'], None))
pathway_list.append(("L-arabinose to D-xylulose-5P", ['L-Arabinose'],
['D-Xylulose-5P'], None))
pathway_list.append(("L-xylulose to D-xylulose-5P", ['L-Xylulose'],
['D-Xylulose-5P'], None))
pathway_list.append(
("ribitol to D-xylulose-5P", ['Ribitol'], ['D-Xylulose-5P'], None))
pathway_list.append(
("D-ribose to D-xylulose-5P", ['D-Ribose'], ['D-Xylulose-5P'], None))
pathway_list.append(("Oxaloacetate to 2-Ketoglutarate", ['Oxaloacetate'],
['2-Ketoglutarate'], None))
pathway_list.append(
("Citrate to 2-Ketoglutarate", ['Citrate'], ['2-Ketoglutarate'], None))
pathway_list.append(
("Pentose Phosphate", ['D-Xylulose-5P + D-Xylulose-5P + D-Ribose-5P'],
['D-Fructose-6P + D-Fructose-6P + D-Glyceraldehyde-3P'], None))
#pathway_list.append(("Pentose Phosphate", ['D-Xylulose-5P + D-Ribose-5P'], ['D-Glyceraldehyde-3P + D-Sedoheptulose-7P'], None))
pathway_list.append(
("D-glucose to D-ribulose-5P", ['D-Glucose'], ['D-Ribulose-5P'], None))
pathway_list.append(("D-glucose to D-fructose-16P", ['D-Glucose'],
['D-Fructose-16P'], None))
pathway_list.append(("D-fructose-6P to GAP+DHAP", ['D-Fructose-6P'],
['D-Glyceraldehyde-3P + Dihydroxyacetone-3P'], None))
pathway_list.append(
("GAP to 3-PG", ['D-Glyceraldehyde-3P'], ['3-Phosphoglycerate'], None))
pathway_list.append(
("GAP to 2-PG", ['D-Glyceraldehyde-3P'], ['2-Phosphoglycerate'], None))
pathway_list.append(
("BPG to 3-PG", ['Bisphosphoglycerate'], ['3-Phosphoglycerate'], None))
pathway_list.append(
("BPG to 2-PG", ['Bisphosphoglycerate'], ['2-Phosphoglycerate'], None))
pathway_list.append(
("BPG to PEP", ['Bisphosphoglycerate'], ['Phosphoenolpyruvate'], None))
pathway_list.append(
("3-PG to PYR", ['3-Phosphoglycerate'], ['Pyruvate'], None))
# Biosynthesis
pathway_list.append(
("3-PG to L-Serine", ['3-Phosphoglycerate'], ['L-Serine'], None))
pathway_list.append(
("L-Serine to Glycine", ['L-Serine'], ['Glycine'], None))
pathway_list.append(
("Pyruvate to L-Alanine", ['Pyruvate'], ['L-Alanine'], None))
pathway_list.append(
("Synthesis of L-Serine", Biosynthese_compounds, ['L-Serine'], None))
pathway_list.append(
("Synthesis of L-Alanine", Biosynthese_compounds, ['L-Alanine'], None))
pathway_list.append(
("Synthesis of Glycine", Biosynthese_compounds, ['Glycine'], None))
pathway_list.append(("Synthesis of L-Aspartate", Biosynthese_compounds,
['L-Aspartate'], None))
pathway_list.append(("Synthesis of L-Glutamate", Biosynthese_compounds,
['L-Glutamate'], None))
# Pentose utilization
pathway_list.append(("L-Arabinose to Pentose Phosphate", ['L-Arabinose'],
Pentose_target_compounds, None))
pathway_list.append(("D-Xylose to Pentose Phosphate", ['D-Xylose'],
Pentose_target_compounds, None))
pathway_list.append(("D-Ribose to Pentose Phosphate", ['D-Ribose'],
Pentose_target_compounds, None))
pathway_list.append(("Ribitol to Pentose Phosphate", ['Ribitol'],
Pentose_target_compounds, None))
pathway_list.append(("D-Arabitol to Pentose Phosphate", ['D-Arabitol'],
Pentose_target_compounds, None))
# Glycolysis
pathway_list.append(
("GAP to PYR", ['D-Glyceraldehyde-3P'], ['Pyruvate'], 'PP'))
pathway_list.append(("GAP to DHAP", ['D-Glyceraldehyde-3P'],
['Dihydroxyacetone-3P'], 'PP'))
pathway_list.append(("GAP to PEP", ['D-Glyceraldehyde-3P + H2O'],
['Phosphoenolpyruvate + H2O'], 'PP'))
pathway_list.append(
("GAP to 2-PG", ['D-Glyceraldehyde-3P'], ['2-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC to GAP", ['D-Glucose'],
['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(
("GLC to PYR", ['Glucose'], ['Pyruvate + Pyruvate'], 'PP'))
pathway_list.append(("GLC-36P to GAP", ['D-Glucose-36P'],
['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(("GLC-6P to GAP", ['D-Glucose-6P'],
['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(("GLC-6P to BPG", ['D-Glucose-6P'],
['Bisphosphoglycerate + Bisphosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to 3-PG", ['D-Glucose-6P'],
['3-Phosphoglycerate + 3-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to 2-PG", ['D-Glucose-6P'],
['2-Phosphoglycerate + 2-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to PEP", ['D-Glucose-6P'],
['Phosphoenolpyruvate + Phosphoenolpyruvate'], 'PP'))
pathway_list.append(
("2-PG to PYR", ['2-Phosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(
("3-PG to PYR", ['3-Phosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(
("BPG to PYR", ['Bisphosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(
("BPG to PEP", ['Bisphosphoglycerate'], ['Phosphoenolpyruvate'], 'PP'))
# Arren's project
pathway_list.append(
("Glyoxylate + GAP to Pentose", ['Glyoxylate + D-Glyceraldehyde-3P'],
Pentose_target_compounds, None))
pathway_list.append(
("Glyoxylate + PYR to Pentose", ['Glyoxylate + Pyruvate'],
Pentose_target_compounds, None))
pathway_list.append(
("Glycolate + GAP to Pentose", ['Glycolate + D-Glyceraldehyde-3P'],
Pentose_target_compounds, None))
pathway_list.append(
("Glycolate + PYR to Pentose", ['Glycolate + Pyruvate'],
Pentose_target_compounds, None))
pathway_names = [pathway[0] for pathway in pathway_list]
pathway_map = {}
for pathway in pathway_list:
pathway_map[pathway[0]] = pathway[1:]
if (len(sys.argv) > 1):
pathway_name = pathway_names[int(sys.argv[1]) - 1]
else:
pathway_name = TkListSelection(pathway_names, "Choose a pathway:")
if (pathway_name == None):
sys.exit(0)
(substrates, products, pruning_method) = pathway_map[pathway_name]
pathfinder = PathFinder(carbon_only=False,
pruning_method=pruning_method,
ignore_chirality=True)
print "-" * 80 + "\nChosen pathway name is %s\n" % pathway_name + "-" * 80
util._mkdir("../results")
util._mkdir("../results/" + pathway_name)
html_writer = HtmlWriter("../results/" + pathway_name + ".html")
html_writer.write("<h1><center>%s</center></h1>\n" % pathway_name)
html_writer.write("<ul>\n")
for i in range(len(substrates)):
for j in range(len(products)):
##(subs, prod) = pathfinder.balance_reaction(substrates[i], products[j])
(subs, prod) = (substrates[i], products[j])
pathway_prefix = "pathway_%d_%d" % (i, j)
util._mkdir("../results/" + pathway_name + "/" + pathway_prefix)
pathfinder.solve_pathway(subs,
prod,
html_writer,
pathway_name,
pathway_prefix,
max_levels=6,
stop_after_first_solution=True)
html_writer.flush()
html_writer.write("</ul>\n")
html_writer.display()
return
def make_welcome_page(self, mailaddr, filename):
db = Database()
try:
prob = db.get_properties('maildata')
user_name = prob[mailaddr]
writer = HtmlWriter(open(filename, mode='w'))
writer.title('Welcome to ' + user_name + 'is page!')
writer.paragraph(user_name + 'のページへようこそ。')
writer.paragraph('メール待っていますね。')
writer.mailto(mailaddr, user_name)
writer.close()
print(filename + ' ' + 'is created for' + mailaddr + ' ' + '(' +
user_name + ')')
except IOError as e:
logging.exception(e)
def make_welcome_page(mailaddr, filename):
# try:
# username = '******'
# mailaddr = 'mailaddr'
mailprop = Database.get_properties('maildata')
# print mailprop
username = mailprop.get('user', mailaddr)
myfile = open(filename, 'w')
writer = HtmlWriter(myfile)
writer.title('Welcom to ' + username + ' s page!')
writer.paragraph(username + 'のページへようこそ。')
writer.paragraph('メールまっていますね。')
writer.mailto(mailaddr, username)
writer.closing()
print filename + ' is created for ' + mailaddr + ' (' + username + ')'
#!/usr/bin/python
import sys
import os
import util
from chemconvert import hash2graph
from html_writer import HtmlWriter
from svg import Scene
html = HtmlWriter("../results/hash_list.html")
util._mkdir("../results/hash_list")
for line in util.parse_text_file(sys.argv[1]):
print line
graph = hash2graph(line)
graph.initialize_pos()
scene = graph.svg(Scene(200, 200, font_size=12))
html.write_svg(scene, "../results/hash_list/" + line)
html.display()
def write_recipients_page(html_dir,
recipients_filename,
funder1,
funder2,
recipients):
html_fp = open(os.path.join(html_dir,recipients_filename), 'w')
html_writer = HtmlWriter(html_fp)
html_writer.open_tag('html')
html_writer.open_tag('head')
html_writer.empty_tag('link', {'href': 'style.css',
'rel':'stylesheet'})
html_writer.empty_tag('link', {'href': 'overlap.css',
'rel':'stylesheet'})
html_writer.close_tag('head')
html_writer.open_tag('body')
html_writer.open_tag('div', {'class': 'container'})
html_writer.write_html('h1',
'Recipients common to {0} and {1}'.format(funder1,
funder2))
html_writer.open_tag('ul')
for recipient in recipients:
html_writer.write_html('li', recipient)
html_writer.close_tag('ul')
html_writer.close_tag('div')
html_writer.close_tag('body')
html_writer.close_tag('html')
html_fp.close()
class Pathologic:
def __init__(self, modules_file, experiment_name, max_module_size=6):
self.modules_file = modules_file
self.experiment_name = experiment_name
self.max_module_size = max_module_size
util._mkdir("../log")
self.logfile = open("../log/pathologic_" + self.experiment_name + ".log", "w")
util._mkdir("../results")
util._mkdir("../results/pathologic_" + self.experiment_name)
self.html_writer = HtmlWriter("../results/pathologic_" + self.experiment_name + ".html")
self.html_writer.write("<h1>List of optimal and non-optimal pathways</h1>\n")
self.html_writer.write("<ul>\n")
self.line_counter = 0
self.pathfinder = None
def find_shortest_pathways(self, substrate, product, max_levels, stop_after_first_solution=True):
G_subs = compound2graph(substrate)
G_prod = compound2graph(product)
return self.pathfinder.find_shortest_pathway([G_subs], [G_prod], max_levels=max_levels, stop_after_first_solution=stop_after_first_solution)
def get_distance(self, substrate, product, max_levels):
G_subs = compound2graph(substrate)
G_prod = compound2graph(product)
return self.pathfinder.find_distance([G_subs], [G_prod], max_levels=max_levels)
def get_all_pathways(self, substrate, product, max_levels):
"""return all existing pathways with a length <= max_distance.
"""
(original_compound_map, possible_pathways, min_length) = self.find_shortest_pathways(substrate, product, max_levels=max_levels, stop_after_first_solution=False)
if (possible_pathways == []):
return []
else:
scene_list = self.pathfinder.get_all_possible_scenes(original_compound_map, possible_pathways)
return [scene for (cost, scene) in scene_list] # strip off the cost of each pathway
def get_shortest_pathways(self, substrate, product, max_levels):
"""return -1 if the is no path with length <= max_levels.
otherwise a pair containing a list of all pathways with the minimal length and the minimal length itself
"""
(original_compound_map, possible_pathways, min_length) = self.find_shortest_pathways(substrate, product, max_levels=max_levels, stop_after_first_solution=True)
if (possible_pathways == []):
return ([], -1)
else:
scene_list = self.pathfinder.get_all_possible_scenes(original_compound_map, possible_pathways)
return ([scene for (cost, scene) in scene_list], min_length) # strip off the cost of each pathway
def verify_pathway(self, pathway):
sys.stdout.write("Verifying pathway: %s\n" % str(pathway))
for i in range(len(pathway)-1):
sys.stdout.write(" - checking '%s' -> '%s' ... " % tuple(pathway[i:i+2]))
sys.stdout.flush()
distance = self.get_distance(pathway[i], pathway[i+1], 1)
if (distance == -1):
sys.stdout.write("FAILED (not neighbors)\n")
else:
sys.stdout.write("OK\n")
sys.stdout.flush()
def is_optimal(self, pathway, i, j, draw_scenes=False):
sys.stdout.write(str(pathway[i:j+1]) + " ... ")
wt_distance = j - i
if (wt_distance <= 1): # we have verified that this is optimal already
return True
if (draw_scenes):
# try to find at least one path which is shorter than the wild-type path:
(scenes, dist) = self.get_shortest_pathways(pathway[i], pathway[j], wt_distance - 1)
else:
dist = self.get_distance(pathway[i], pathway[j], wt_distance - 1)
if (dist == -1): # which means no shorter path has been found, hence the WT pathway is one of the shortest
sys.stdout.write(" is optimal!\n")
self.html_writer.write("<li><span style=color:green>%s - OPTIMAL</span></li>\n" % str(pathway[i:j+1]))
self.html_writer.flush()
return True
else: # there is a shorter path than the WT one
sys.stdout.write(" is not optimal!\n")
self.html_writer.write("<li>\n <span style=color:red>%s - NOT OPTIMAL</span><br>\n " % str(pathway[i:j+1]))
if (draw_scenes):
#for s in scenes:
s = scenes[0] # draws only the first scene (otherwise it could be too long)
self.html_writer.write_svg(s, "pathologic_" + self.experiment_name + "/pathway_%d_%d_%d" % (self.line_counter, i, j))
self.html_writer.write("\n</li>\n")
self.html_writer.flush()
return False
def find_modules(self, pathway, draw_scenes=False):
self.verify_pathway(pathway)
i = 0
for j in range(2, len(pathway)):
if (j - i >= self.max_module_size):
sys.stdout.write(str(pathway[i:(j+1)]) + " is too long for me, chopping off the head...\n")
i += 1
# shorten the path from it's head until it is optimal (or too short, i.e. length=1)
while ( (j - i) > 1 and (not self.is_optimal(pathway, i, j, draw_scenes=draw_scenes)) ):
i += 1
def analyze(self, carbon_only=True, use_antimotifs=True, draw_scenes=False):
for line in util.parse_text_file("../rec/" + self.modules_file + ".txt"):
if (line[0] == '@'):
line = line[1:]
self.pathfinder = PathFinder(carbon_only=carbon_only, pruning_method=None, ignore_chirality=False, use_antimotifs=use_antimotifs, outstream=self.logfile)
else:
self.pathfinder = PathFinder(carbon_only=carbon_only, pruning_method=None, ignore_chirality=True, use_antimotifs=use_antimotifs, outstream=self.logfile)
pathway = line.split(';')
self.find_modules(pathway, draw_scenes=draw_scenes)
self.line_counter += 1
def analyze_pairs(self, carbon_only=True, use_antimotifs=True, max_distance=4):
distances = [] # the minimal pathway length between the substrate and the product
alternatives = [] # each value is the number of alternative pathways with the minimal distance
line_counter = 0
for line in util.parse_text_file("../rec/" + self.modules_file + ".txt"):
if (line[0] == '@'):
line = line[1:]
self.pathfinder = PathFinder(carbon_only=carbon_only, pruning_method=None, ignore_chirality=False, use_antimotifs=use_antimotifs, outstream=self.logfile)
else:
self.pathfinder = PathFinder(carbon_only=carbon_only, pruning_method=None, ignore_chirality=True, use_antimotifs=use_antimotifs, outstream=self.logfile)
(subs, prod, max_steps) = line.split(";", 2)
if (max_steps in ['-1','inf','']):
sys.stdout.write(subs + " -(?)-> " + prod)
sys.stdout.flush()
(scenes, dist) = self.get_shortest_pathways(subs, prod, max_distance)
else:
dist = int(max_steps)
sys.stdout.write(subs + " -(%d)-> " % dist + prod)
sys.stdout.flush()
scenes = self.get_all_pathways(subs, prod, dist)
if (dist == -1):
sys.stdout.write(", Distance(L) = inf, N = 0\n")
sys.stdout.flush()
distances.append("inf")
alternatives.append(0)
self.html_writer.write("<li><span style=color:red>%s <-> %s (distance > %d)</span></li>\n" % (subs, prod, self.max_module_size))
self.html_writer.flush()
else:
sys.stdout.write(", Distance(L) = %d, N = %d\n" % (dist, len(scenes)))
sys.stdout.flush()
distances.append(dist)
alternatives.append(len(scenes))
self.html_writer.write("<li><span style=color:green>%s <-> %s (distance = %d)</span></li>\n" % (subs, prod, dist))
for i in range(len(scenes)):
self.html_writer.write("<li>")
self.html_writer.write_svg(scenes[i], "pathologic_" + self.experiment_name + "/pair%d_path%d" % (line_counter,i))
self.html_writer.write("</li>\n")
self.html_writer.flush()
line_counter += 1
result_file = open("../results/" + self.experiment_name + ".txt", "w")
result_file.write(str(distances) + "\n" + str(alternatives) + "\n")
result_file.close()
def display(self):
self.html_writer.write("</ul>\n")
self.html_writer.write(self.pathfinder.reactor.antimotif_summary())
self.html_writer.display()
def __del__(self):
del self.pathfinder
self.logfile.close()
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 options_html():
"""HTML options page.
Shows bunch of options for conversion to HTML format. Converts to HTML
using those options.
"""
if session.input_data_id is None: redirect(URL(index))
# get
input_data = db.input_data(session.input_data_id)
options = []
options += [(T('Page width') + ': ', INPUT(_type='number', _name='html_page_width', _style='width: 50px', _value=_optval('html_page_width', '800')) +
' ' + T('pixels'))]
options += [(T('Image size') + ': ',
INPUT(_type='number', _name='html_image_width', _style='width: 50px', _value=_optval('html_image_width', '300')) +
' x ' +
INPUT(_type='number', _name='html_image_height', _style='width: 50px', _value=_optval('html_image_height', '300')) +
' ' + T('pixels'))]
checked = _optval('html_image_upscale', '') == 'checked'
options += [(T('Upscale images') + ': ',
INPUT(_type='checkbox', _name='html_image_upscale', _value='checked', value=checked))]
fmtoptions = [OPTION(T('31.12.2001'), _value='DMY.'),
OPTION(T('31/12/2001'), _value='DMY/'),
OPTION(T('2001-12-31'), _value='YMD-'),
OPTION(T('12/31/2012'), _value='MDY/')]
options += [(T('Date format') + ': ', SELECT(*fmtoptions, _name='datefmt', value=_optval('datefmt', 'DMY.'), _style='width: 150px'))]
options += [('', INPUT(_value=T('Start'), _type='submit'))]
form = FORM(TABLE(*[TR(*[TD(o) for o in opt]) for opt in options]))
if form.process().accepted:
# remember options
session.options = form.vars
# all config options
config = ftp_config.Config()
config['page_width'] = form.vars.html_page_width + 'px'
config['image_width'] = form.vars.html_image_width + 'px'
config['image_height'] = form.vars.html_image_height + 'px'
config['image_upscale'] = form.vars.html_image_upscale == 'checked'
config['date_format'] = form.vars.datefmt
# convert it
floc = FileLocator(os.path.join(request.folder, 'uploads', input_data.input_file))
reader = DrevoReader(floc)
output = tempfile.TemporaryFile()
writer = HtmlWriter(floc, output, config)
writer.write(reader)
# send the file
output.seek(0)
response.headers['Content-Type'] = 'text/html'
output_name = os.path.splitext(input_data.original_name)[0] + '.html'
return response.stream(output, 1048576, attachment=True, filename=output_name)
return dict(form=form)
from cobra.io.sbml import create_cobra_model_from_sbml_file
from html_writer import HtmlWriter
import analysis_toolbox
main_html = HtmlWriter('res/fba.html')
main_html.write('<title>FBA</title>')
main_html.write('<h1>FBA</h1>\n')
model = create_cobra_model_from_sbml_file('data/iJO1366.xml')
main_html.write('</ul>\n')
print("Running standard FBA...")
solution = model.optimize()
if solution.status is not None:
# write the flux summary for the knockout model as HTML
analysis_toolbox.model_summary(model, solution, main_html)
main_html.close()
def make_welcome_page(self, mailaddr, filename):
db = Database()
try:
prob = db.get_properties('maildata')
user_name = prob[mailaddr]
writer = HtmlWriter(open(filename, mode='w'))
writer.title('Welcome to ' + user_name + 'is page')
writer.paragraph(user_name + 'のページへようこそ。')
writer.paragraph('メール持っていますね。')
writer.mailto(mailaddr, user_name)
writer.close()
print(filename + ' is created for' + mailaddr + ' (' + user_name + ')')
except IOError as e:
logging.exception(e)
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():
Pentose_target_compounds = ['D-Xylulose-5P', 'D-Ribulose-5P', 'D-Ribose-5P']
PP_compounds = \
['D-Xylulose-5P',\
'D-Ribulose-5P',\
'D-Ribose-5P',\
'D-Erythrose-4P',\
'D-Sedoheptulose-7P',\
'D-Fructose-6P',\
'D-Glyceraldehyde-3P']
Glycolysis_compounds = \
['Dihydroxyacetone-3P',\
'D-Glyceraldehyde-3P',\
'Bisphosphoglycerate',\
'3-Phosphoglycerate',\
'2-Phosphoglycerate',\
'Phosphoenolpyruvate',\
'Pyruvate']
TCA_compounds = \
['Oxaloacetate',\
'Citrate',\
'cis-Aconitate',\
'D-Isocitrate',\
'2-Ketoglutarate',\
#succinyl-CoA\
'Succinate',\
'Fumarate',\
'Malate',\
]
Biosynthese_compounds = PP_compounds + Glycolysis_compounds + TCA_compounds
pathway_list = []
# fast test
pathway_list.append(("TEST", ['2-Phosphoglycerate'], ['Bisphosphoglycerate'], None))
# Optimality Modules:
pathway_list.append(("Glucose to Fructose", ['D-glucose-6P'], ['D-fructose-6P'], None))
pathway_list.append(("Fructose to Glucose", ['D-fructose-6P'], ['D-glucose-6P'], None))
pathway_list.append(("oxaloacetate+acetyl-CoA to citrate", ['oxaloacetate + acetyl-CoA'], ['citrate'], None))
pathway_list.append(("cis-aconitate to succinate", ['cis-aconitate'], ['succinate'], None))
pathway_list.append(("D-xylose to D-xylulose-5P", ['D-Xylose'], ['D-Xylulose-5P'], None))
pathway_list.append(("D-arabitol to D-xylulose-5P", ['D-Arabitol'], ['D-Xylulose-5P'], None))
pathway_list.append(("L-arabinose to D-xylulose-5P", ['L-Arabinose'], ['D-Xylulose-5P'], None))
pathway_list.append(("L-xylulose to D-xylulose-5P", ['L-Xylulose'], ['D-Xylulose-5P'], None))
pathway_list.append(("ribitol to D-xylulose-5P", ['Ribitol'], ['D-Xylulose-5P'], None))
pathway_list.append(("D-ribose to D-xylulose-5P", ['D-Ribose'], ['D-Xylulose-5P'], None))
pathway_list.append(("Oxaloacetate to 2-Ketoglutarate", ['Oxaloacetate'], ['2-Ketoglutarate'], None))
pathway_list.append(("Citrate to 2-Ketoglutarate", ['Citrate'], ['2-Ketoglutarate'], None))
pathway_list.append(("Pentose Phosphate", ['D-Xylulose-5P + D-Xylulose-5P + D-Ribose-5P'], ['D-Fructose-6P + D-Fructose-6P + D-Glyceraldehyde-3P'], None))
#pathway_list.append(("Pentose Phosphate", ['D-Xylulose-5P + D-Ribose-5P'], ['D-Glyceraldehyde-3P + D-Sedoheptulose-7P'], None))
pathway_list.append(("D-glucose to D-ribulose-5P", ['D-Glucose'], ['D-Ribulose-5P'], None))
pathway_list.append(("D-glucose to D-fructose-16P", ['D-Glucose'], ['D-Fructose-16P'], None))
pathway_list.append(("D-fructose-6P to GAP+DHAP", ['D-Fructose-6P'], ['D-Glyceraldehyde-3P + Dihydroxyacetone-3P'], None))
pathway_list.append(("GAP to 3-PG", ['D-Glyceraldehyde-3P'], ['3-Phosphoglycerate'], None))
pathway_list.append(("GAP to 2-PG", ['D-Glyceraldehyde-3P'], ['2-Phosphoglycerate'], None))
pathway_list.append(("BPG to 3-PG", ['Bisphosphoglycerate'], ['3-Phosphoglycerate'], None))
pathway_list.append(("BPG to 2-PG", ['Bisphosphoglycerate'], ['2-Phosphoglycerate'], None))
pathway_list.append(("BPG to PEP", ['Bisphosphoglycerate'], ['Phosphoenolpyruvate'], None))
pathway_list.append(("3-PG to PYR", ['3-Phosphoglycerate'], ['Pyruvate'], None))
# Biosynthesis
pathway_list.append(("3-PG to L-Serine", ['3-Phosphoglycerate'], ['L-Serine'], None))
pathway_list.append(("L-Serine to Glycine", ['L-Serine'], ['Glycine'], None))
pathway_list.append(("Pyruvate to L-Alanine", ['Pyruvate'], ['L-Alanine'], None))
pathway_list.append(("Synthesis of L-Serine", Biosynthese_compounds, ['L-Serine'], None))
pathway_list.append(("Synthesis of L-Alanine", Biosynthese_compounds, ['L-Alanine'], None))
pathway_list.append(("Synthesis of Glycine", Biosynthese_compounds, ['Glycine'], None))
pathway_list.append(("Synthesis of L-Aspartate", Biosynthese_compounds, ['L-Aspartate'], None))
pathway_list.append(("Synthesis of L-Glutamate", Biosynthese_compounds, ['L-Glutamate'], None))
# Pentose utilization
pathway_list.append(("L-Arabinose to Pentose Phosphate", ['L-Arabinose'], Pentose_target_compounds, None))
pathway_list.append(("D-Xylose to Pentose Phosphate", ['D-Xylose'], Pentose_target_compounds, None))
pathway_list.append(("D-Ribose to Pentose Phosphate", ['D-Ribose'], Pentose_target_compounds, None))
pathway_list.append(("Ribitol to Pentose Phosphate", ['Ribitol'], Pentose_target_compounds, None))
pathway_list.append(("D-Arabitol to Pentose Phosphate", ['D-Arabitol'], Pentose_target_compounds, None))
# Glycolysis
pathway_list.append(("GAP to PYR", ['D-Glyceraldehyde-3P'], ['Pyruvate'], 'PP'))
pathway_list.append(("GAP to DHAP", ['D-Glyceraldehyde-3P'], ['Dihydroxyacetone-3P'], 'PP'))
pathway_list.append(("GAP to PEP", ['D-Glyceraldehyde-3P + H2O'], ['Phosphoenolpyruvate + H2O'], 'PP'))
pathway_list.append(("GAP to 2-PG", ['D-Glyceraldehyde-3P'], ['2-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC to GAP", ['D-Glucose'], ['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(("GLC to PYR", ['Glucose'], ['Pyruvate + Pyruvate'], 'PP'))
pathway_list.append(("GLC-36P to GAP", ['D-Glucose-36P'], ['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(("GLC-6P to GAP", ['D-Glucose-6P'], ['D-Glyceraldehyde-3P + D-Glyceraldehyde-3P'], 'PP'))
pathway_list.append(("GLC-6P to BPG", ['D-Glucose-6P'], ['Bisphosphoglycerate + Bisphosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to 3-PG", ['D-Glucose-6P'], ['3-Phosphoglycerate + 3-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to 2-PG", ['D-Glucose-6P'], ['2-Phosphoglycerate + 2-Phosphoglycerate'], 'PP'))
pathway_list.append(("GLC-6P to PEP", ['D-Glucose-6P'], ['Phosphoenolpyruvate + Phosphoenolpyruvate'], 'PP'))
pathway_list.append(("2-PG to PYR", ['2-Phosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(("3-PG to PYR", ['3-Phosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(("BPG to PYR", ['Bisphosphoglycerate'], ['Pyruvate'], 'PP'))
pathway_list.append(("BPG to PEP", ['Bisphosphoglycerate'], ['Phosphoenolpyruvate'], 'PP'))
# Arren's project
pathway_list.append(("Glyoxylate + GAP to Pentose", ['Glyoxylate + D-Glyceraldehyde-3P'], Pentose_target_compounds, None))
pathway_list.append(("Glyoxylate + PYR to Pentose", ['Glyoxylate + Pyruvate'], Pentose_target_compounds, None))
pathway_list.append(("Glycolate + GAP to Pentose", ['Glycolate + D-Glyceraldehyde-3P'], Pentose_target_compounds, None))
pathway_list.append(("Glycolate + PYR to Pentose", ['Glycolate + Pyruvate'], Pentose_target_compounds, None))
pathway_names = [pathway[0] for pathway in pathway_list]
pathway_map = {}
for pathway in pathway_list:
pathway_map[pathway[0]] = pathway[1:]
if (len(sys.argv) > 1):
pathway_name = pathway_names[int(sys.argv[1]) - 1]
else:
pathway_name = TkListSelection(pathway_names, "Choose a pathway:")
if (pathway_name == None):
sys.exit(0)
(substrates, products, pruning_method) = pathway_map[pathway_name]
pathfinder = PathFinder(carbon_only=False, pruning_method=pruning_method, ignore_chirality=True)
print "-"*80 + "\nChosen pathway name is %s\n" % pathway_name + "-"*80
util._mkdir("../results")
util._mkdir("../results/" + pathway_name)
html_writer = HtmlWriter("../results/" + pathway_name + ".html")
html_writer.write("<h1><center>%s</center></h1>\n" % pathway_name)
html_writer.write("<ul>\n")
for i in range(len(substrates)):
for j in range(len(products)):
##(subs, prod) = pathfinder.balance_reaction(substrates[i], products[j])
(subs, prod) = (substrates[i], products[j])
pathway_prefix = "pathway_%d_%d" % (i, j)
util._mkdir("../results/" + pathway_name + "/" + pathway_prefix)
pathfinder.solve_pathway(subs, prod, html_writer, pathway_name, pathway_prefix, max_levels=6, stop_after_first_solution=True)
html_writer.flush()
html_writer.write("</ul>\n")
html_writer.display()
return
max_recipients = 1560
max_heatmap_index = 8.0
html_file = os.path.join(output_dir, 'overlap.html')
html_fp = open(html_file, 'w')
json_fp = open(json_file, 'rb')
results = json.load(json_fp)
all_results = {}
funders = results.keys()
funders.sort()
html_writer = HtmlWriter(html_fp)
html_writer.open_tag('html')
html_writer.open_tag('head')
html_writer.empty_tag('link', {'href': 'style.css',
'rel':'stylesheet'})
html_writer.empty_tag('link', {'href': 'overlap.css',
'rel':'stylesheet'})
html_writer.close_tag('head')
html_writer.open_tag('body')
html_writer.open_tag('div', {'class': 'container'})
html_writer.write_html('h1', 'Overlap between funders')
html_writer.open_tag('table')
html_writer.open_tag('tr')
#!/usr/bin/python
import sys
import os
import util
from chemconvert import hash2graph
from html_writer import HtmlWriter
from svg import Scene
html = HtmlWriter("../results/hash_list.html")
util._mkdir("../results/hash_list")
for line in util.parse_text_file(sys.argv[1]):
print line
graph = hash2graph(line)
graph.initialize_pos()
scene = graph.svg(Scene(200, 200, font_size=12))
html.write_svg(scene, "../results/hash_list/" + line)
html.display()
sns.set()
sns.axes_style("darkgrid")
l = logging.getLogger()
l.setLevel(logging.INFO)
exp_name = "ecoli_ccm_aerobic_channeling"
DATA_DIR = os.path.expanduser("~/git/enzyme-cost/data")
RES_DIR = os.path.expanduser("~/git/enzyme-cost/res")
model_sbtab_fpath = os.path.join(DATA_DIR, "%s_ModelData.csv" % exp_name)
verify_sbtab_fpath = os.path.join(DATA_DIR, "%s_ValidationData.csv" % exp_name)
sqlite_fpath = os.path.join(RES_DIR, "%s.sqlite" % exp_name)
mat_fpath = os.path.join(RES_DIR, "%s.mat" % exp_name)
html_fpath = os.path.join(RES_DIR, "%s.html" % exp_name)
html = HtmlWriter(html_fpath)
logging.info("Converting input data from SBtab to SQLite")
_model_sbtab_dict = SBtabDict.FromSBtab(model_sbtab_fpath)
_verify_sbtab_dict = SBtabDict.FromSBtab(verify_sbtab_fpath)
comm = sqlite3.connect(sqlite_fpath)
_model_sbtab_dict.SBtab2SQL(comm, append=False)
_verify_sbtab_dict.SBtab2SQL(comm, append=False)
comm.commit()
comm.close()
logging.info("Reading data from SQLite database: " + sqlite_fpath)
sbtab_dict = SBtabDict.FromSQLite(sqlite_fpath)
logging.info("Creating an ECM model using the data")
model = ECMmodel(sbtab_dict, dG0_source="keq_table")
logging.info("Exporting data to .mat file: " + mat_fpath)
class Pathologic:
def __init__(self, modules_file, experiment_name, max_module_size=6):
self.modules_file = modules_file
self.experiment_name = experiment_name
self.max_module_size = max_module_size
util._mkdir("../log")
self.logfile = open(
"../log/pathologic_" + self.experiment_name + ".log", "w")
util._mkdir("../results")
util._mkdir("../results/pathologic_" + self.experiment_name)
self.html_writer = HtmlWriter("../results/pathologic_" +
self.experiment_name + ".html")
self.html_writer.write(
"<h1>List of optimal and non-optimal pathways</h1>\n")
self.html_writer.write("<ul>\n")
self.line_counter = 0
self.pathfinder = None
def find_shortest_pathways(self,
substrate,
product,
max_levels,
stop_after_first_solution=True):
G_subs = compound2graph(substrate)
G_prod = compound2graph(product)
return self.pathfinder.find_shortest_pathway(
[G_subs], [G_prod],
max_levels=max_levels,
stop_after_first_solution=stop_after_first_solution)
def get_distance(self, substrate, product, max_levels):
G_subs = compound2graph(substrate)
G_prod = compound2graph(product)
return self.pathfinder.find_distance([G_subs], [G_prod],
max_levels=max_levels)
def get_all_pathways(self, substrate, product, max_levels):
"""return all existing pathways with a length <= max_distance.
"""
(original_compound_map, possible_pathways,
min_length) = self.find_shortest_pathways(
substrate,
product,
max_levels=max_levels,
stop_after_first_solution=False)
if (possible_pathways == []):
return []
else:
scene_list = self.pathfinder.get_all_possible_scenes(
original_compound_map, possible_pathways)
return [scene for (cost, scene) in scene_list
] # strip off the cost of each pathway
def get_shortest_pathways(self, substrate, product, max_levels):
"""return -1 if the is no path with length <= max_levels.
otherwise a pair containing a list of all pathways with the minimal length and the minimal length itself
"""
(original_compound_map, possible_pathways,
min_length) = self.find_shortest_pathways(
substrate,
product,
max_levels=max_levels,
stop_after_first_solution=True)
if (possible_pathways == []):
return ([], -1)
else:
scene_list = self.pathfinder.get_all_possible_scenes(
original_compound_map, possible_pathways)
return ([scene for (cost, scene) in scene_list], min_length
) # strip off the cost of each pathway
def verify_pathway(self, pathway):
sys.stdout.write("Verifying pathway: %s\n" % str(pathway))
for i in range(len(pathway) - 1):
sys.stdout.write(" - checking '%s' -> '%s' ... " %
tuple(pathway[i:i + 2]))
sys.stdout.flush()
distance = self.get_distance(pathway[i], pathway[i + 1], 1)
if (distance == -1):
sys.stdout.write("FAILED (not neighbors)\n")
else:
sys.stdout.write("OK\n")
sys.stdout.flush()
def is_optimal(self, pathway, i, j, draw_scenes=False):
sys.stdout.write(str(pathway[i:j + 1]) + " ... ")
wt_distance = j - i
if (wt_distance <= 1): # we have verified that this is optimal already
return True
if (draw_scenes):
# try to find at least one path which is shorter than the wild-type path:
(scenes,
dist) = self.get_shortest_pathways(pathway[i], pathway[j],
wt_distance - 1)
else:
dist = self.get_distance(pathway[i], pathway[j], wt_distance - 1)
if (
dist == -1
): # which means no shorter path has been found, hence the WT pathway is one of the shortest
sys.stdout.write(" is optimal!\n")
self.html_writer.write(
"<li><span style=color:green>%s - OPTIMAL</span></li>\n" %
str(pathway[i:j + 1]))
self.html_writer.flush()
return True
else: # there is a shorter path than the WT one
sys.stdout.write(" is not optimal!\n")
self.html_writer.write(
"<li>\n <span style=color:red>%s - NOT OPTIMAL</span><br>\n "
% str(pathway[i:j + 1]))
if (draw_scenes):
#for s in scenes:
s = scenes[
0] # draws only the first scene (otherwise it could be too long)
self.html_writer.write_svg(
s, "pathologic_" + self.experiment_name +
"/pathway_%d_%d_%d" % (self.line_counter, i, j))
self.html_writer.write("\n</li>\n")
self.html_writer.flush()
return False
def find_modules(self, pathway, draw_scenes=False):
self.verify_pathway(pathway)
i = 0
for j in range(2, len(pathway)):
if (j - i >= self.max_module_size):
sys.stdout.write(
str(pathway[i:(j + 1)]) +
" is too long for me, chopping off the head...\n")
i += 1
# shorten the path from it's head until it is optimal (or too short, i.e. length=1)
while (
(j - i) > 1 and
(not self.is_optimal(pathway, i, j, draw_scenes=draw_scenes))):
i += 1
def analyze(self,
carbon_only=True,
use_antimotifs=True,
draw_scenes=False):
for line in util.parse_text_file("../rec/" + self.modules_file +
".txt"):
if (line[0] == '@'):
line = line[1:]
self.pathfinder = PathFinder(carbon_only=carbon_only,
pruning_method=None,
ignore_chirality=False,
use_antimotifs=use_antimotifs,
outstream=self.logfile)
else:
self.pathfinder = PathFinder(carbon_only=carbon_only,
pruning_method=None,
ignore_chirality=True,
use_antimotifs=use_antimotifs,
outstream=self.logfile)
pathway = line.split(';')
self.find_modules(pathway, draw_scenes=draw_scenes)
self.line_counter += 1
def analyze_pairs(self,
carbon_only=True,
use_antimotifs=True,
max_distance=4):
distances = [
] # the minimal pathway length between the substrate and the product
alternatives = [
] # each value is the number of alternative pathways with the minimal distance
line_counter = 0
for line in util.parse_text_file("../rec/" + self.modules_file +
".txt"):
if (line[0] == '@'):
line = line[1:]
self.pathfinder = PathFinder(carbon_only=carbon_only,
pruning_method=None,
ignore_chirality=False,
use_antimotifs=use_antimotifs,
outstream=self.logfile)
else:
self.pathfinder = PathFinder(carbon_only=carbon_only,
pruning_method=None,
ignore_chirality=True,
use_antimotifs=use_antimotifs,
outstream=self.logfile)
(subs, prod, max_steps) = line.split(";", 2)
if (max_steps in ['-1', 'inf', '']):
sys.stdout.write(subs + " -(?)-> " + prod)
sys.stdout.flush()
(scenes,
dist) = self.get_shortest_pathways(subs, prod, max_distance)
else:
dist = int(max_steps)
sys.stdout.write(subs + " -(%d)-> " % dist + prod)
sys.stdout.flush()
scenes = self.get_all_pathways(subs, prod, dist)
if (dist == -1):
sys.stdout.write(", Distance(L) = inf, N = 0\n")
sys.stdout.flush()
distances.append("inf")
alternatives.append(0)
self.html_writer.write(
"<li><span style=color:red>%s <-> %s (distance > %d)</span></li>\n"
% (subs, prod, self.max_module_size))
self.html_writer.flush()
else:
sys.stdout.write(", Distance(L) = %d, N = %d\n" %
(dist, len(scenes)))
sys.stdout.flush()
distances.append(dist)
alternatives.append(len(scenes))
self.html_writer.write(
"<li><span style=color:green>%s <-> %s (distance = %d)</span></li>\n"
% (subs, prod, dist))
for i in range(len(scenes)):
self.html_writer.write("<li>")
self.html_writer.write_svg(
scenes[i], "pathologic_" + self.experiment_name +
"/pair%d_path%d" % (line_counter, i))
self.html_writer.write("</li>\n")
self.html_writer.flush()
line_counter += 1
result_file = open("../results/" + self.experiment_name + ".txt", "w")
result_file.write(str(distances) + "\n" + str(alternatives) + "\n")
result_file.close()
def display(self):
self.html_writer.write("</ul>\n")
self.html_writer.write(self.pathfinder.reactor.antimotif_summary())
self.html_writer.display()
def __del__(self):
del self.pathfinder
self.logfile.close()