def main1():
util._mkdir("../results/ec-list")
counter = 1
for line in util.parse_text_file("../rec/ec-list.smiles"):
print counter, ")", line
smiles2svg(line, "../results/ec-list/%d.svg" % counter)
counter += 1
def main1():
util._mkdir("../results/ec-list")
counter = 1
for line in util.parse_text_file("../rec/ec-list.smiles"):
print counter, ")", line
smiles2svg(line, "../results/ec-list/%d.svg" % counter)
counter += 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 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 init():
global params
params = {}
params[
'MPL'] = 3 # Maximal Path Length. Set to None to restrict all options to have minimal length.
params[
'ID'] = False # Isomerase Down. True will prevent Isomerases from acting on phosphorylated compounds.
params[
'EA'] = False # Epimerase Above. True will prevent Epimerases from acting on unphosphorylated compounds.
params['PECK'] = True # Pentose Epimerases Can only work on Ketoses.
params['3ECK'] = False # 3-Epimerases Can only work on Ketoses.
params['TL'] = 1 # Total Length.
params['NE'] = None # Number of Epimerases (counts isoenzymes twice)
params['NI'] = None # Number of Isomerases (counts isoenzymes twice)
params['ND'] = None # Number of Dehydrogenases (counts isoenzymes twice)
params['NK'] = None # Number of Kinases (counts isoenzymes twice)
params[
'NTE'] = 0 # No Two Epimerases. Number maximum number of same-product epimerases.
params['MIE'] = None # Counts the number of isoenzyme pairs.
params[
'TPD'] = None # Total Phosphorylation Distance. Counts the total number of steps before phosphorylation.
for p in params.keys():
print "%s = %s" % (p, str(params[p]))
global enzyme_types, target, sources, pp_enzymes
# this is the target if the metabolism (in our case the input to the PP cycle)
target = 'D-Ribulose-5P'
# sources are sugars that E. coli can grow on without any other carbon source
sources = [
'Ribitol', 'D-Arabitol', 'L-Xylulose', 'D-Ribose', 'D-Xylose',
'L-Arabinose'
]
ketoses = ['D-Ribulose', 'L-Ribulose', 'D-Xylulose', 'L-Xylulose']
aldoses = [
'D-Ribose', 'L-Ribose', 'D-Arabinose', 'L-Arabinose', 'D-Xylose',
'L-Xylose', 'D-Lyxose', 'L-Lyxose'
]
polyols = ['Ribitol', 'Xylitol', 'D-Arabitol', 'L-Arabitol']
# these are edges that don't have a cost since they are part of the PP cycle
pp_enzymes = [('D-Ribulose-5P', 'D-Ribose-5P'),
('D-Ribulose-5P', 'D-Xylulose-5P')]
pp_enzymes += [(j, i) for (i, j) in pp_enzymes]
global G_pentoses, G_wildtype, G_conjecture
G_wildtype = SparseGraph()
G_conjecture = SparseGraph()
G_pentoses = SparseGraph()
# Add all the known possible enzymes to G_pentoses
enzyme_types = {}
for line in parse_text_file("../rec/pentoses_edges.txt"):
tokens = line.split()
for i in [0, 1]:
compound = tokens[i]
neighbor = tokens[1 - i]
(enzyme_type, i_carbon) = tokens[2].split('-')
# add the kinases
G_pentoses[compound][compound + '-5P'] = 1
enzyme_types[(compound, compound + '-5P')] = "KIN"
G_pentoses[compound + '-5P'][compound] = 1
enzyme_types[(compound + '-5P', compound)] = "KIN"
if (params['EA'] and (enzyme_type == "EPI")):
pass # in EA mode don't use Epimerases on phosphorylated forms
elif (params['PECK'] and (enzyme_type == "EPI")
and (not compound in ketoses)):
pass # in PECK mode don't use Epimerases on non-ketoses
elif (params['3ECK'] and (enzyme_type == "EPI") and (i_carbon == 3)
and (not compound in ketoses)):
pass # in 3ECK mode don't use 3-Epimerases on non-ketoses
else:
G_pentoses[compound][neighbor] = 1
enzyme_types[(compound, neighbor)] = enzyme_type
if (params['ID'] and (enzyme_type == "DHG")):
pass # in ID mode don't use Dehydrogenases on phosphorylated forms
elif (params['PECK'] and (enzyme_type == "EPI")
and (not compound in ketoses)):
pass # in PECK mode don't use Epimerases on non-ketoses
elif (params['3ECK'] and (enzyme_type == "EPI") and (i_carbon == 3)
and (not compound in ketoses)):
pass # in 3ECK mode don't use 3-Epimerases on non-ketoses
else:
G_pentoses[compound + '-5P'][neighbor + '-5P'] = 1
enzyme_types[(compound + '-5P',
neighbor + '-5P')] = enzyme_type
# Change the cost of the PP enzymes to 0
for (i, j) in pp_enzymes:
G_wildtype[i][j] = 0
G_pentoses[i][j] = 0
G_conjecture[i][j] = 0
# these are edges that do exist in E. coli but are not part of the Pentose Phosphate Cycle
# according to KEGG and MetaCyc
wildtype_enzymes = \
[('D-Arabitol', 'D-Xylulose'),\
('D-Xylulose', 'D-Xylulose-5P'),\
('D-Xylose', 'D-Xylulose'),\
('L-Arabinose', 'L-Ribulose'),\
('L-Ribulose', 'L-Ribulose-5P'),\
('L-Xylulose', 'L-Xylulose-5P'),\
('L-Xylulose-5P','L-Ribulose-5P'),\
('L-Ribulose-5P','D-Xylulose-5P'),\
('Ribitol', 'D-Ribulose'),\
('D-Ribulose', 'D-Ribulose-5P'),\
('D-Ribose', 'D-Ribose-5P')]
for (i, j) in wildtype_enzymes:
G_wildtype[i][j] = 1
G_wildtype[j][i] = 1
# these is the conjectured list of non-PP enzymes (where the L-Xylulose path is shorter)
conjecture_enzymes = \
[('D-Arabitol', 'D-Xylulose'),\
('D-Xylulose', 'D-Xylulose-5P'),\
('D-Xylose', 'D-Xylulose'),\
('L-Arabinose', 'L-Ribulose'),\
('L-Ribulose', 'L-Ribulose-5P'),\
('L-Xylulose', 'L-Xylulose-5P'),\
('L-Xylulose-5P','D-Ribulose-5P'),\
('L-Ribulose-5P','D-Xylulose-5P'),\
('Ribitol', 'D-Ribulose'),\
('D-Ribulose', 'D-Ribulose-5P'),\
('D-Ribose', 'D-Ribose-5P')]
for (i, j) in conjecture_enzymes:
G_conjecture[i][j] = 1
G_conjecture[j][i] = 1
import sys
import util
from numpy.random import permutation
##########################################################################################################
# MAIN #
##########################################################################################################
modules = []
compounds = set()
precursors = None
for line in util.parse_text_file('../rec/p-value-modules.txt'):
if (precursors == None): # first line is the set of true precursors
precursors = set(line.split(';'))
print "The Precursors: " + ', '.join(precursors)
continue
module = set(line.split(';'))
modules.append(module)
compounds = compounds.union(module)
print "Module: " + ', '.join(module)
compounds = list(compounds)
overlaps = []
for i in range(len(modules)):
for j in range(i):
overlap = modules[i].intersection(modules[j])
if (overlap != set()):
overlaps.append(overlap)
if (base_atom in group_table):
(bonding_atom, valence, hydrogens, charge) = group_table[base_atom]
return (base_atom, valence, hydrogens, charge, 0)
valence = 0
if (base_atom == atom_wildcard):
valence = 0
elif (not base_atom in valence_table):
raise ChemException("Parsing %s: cannot find the atom '%s' in the valence table" % (atom, base_atom))
else:
valence = valence_table[base_atom]
return (base_atom, valence, hydrogens, charge, chirality)
bond_energy_table = {}
for line in util.parse_text_file(util.get_progdir() + "/../rec/bond_energy.txt"):
(bond_type, energy) = line.split(" " ,1)
if (bond_type.find("-") != -1):
(atom1, atom2) = bond_type.split("-")
order = 1
elif (bond_type.find("=") != -1):
(atom1, atom2) = bond_type.split("=")
order = 2
elif (bond_type.find("#") != -1):
(atom1, atom2) = bond_type.split("#")
order = 3
else:
raise ChemException("unable to parse bond type: " + bond_type)
bond_energy_table[(atom1, atom2, order)] = float(energy)
bond_energy_table[(atom2, atom1, order)] = float(energy)
(bonding_atom, valence, hydrogens, charge) = group_table[base_atom]
return (base_atom, valence, hydrogens, charge, 0)
valence = 0
if base_atom == atom_wildcard:
valence = 0
elif not base_atom in valence_table:
raise ChemException("Parsing %s: cannot find the atom '%s' in the valence table" % (atom, base_atom))
else:
valence = valence_table[base_atom]
return (base_atom, valence, hydrogens, charge, chirality)
bond_energy_table = {}
for line in util.parse_text_file(util.get_progdir() + "/../rec/bond_energy.txt"):
(bond_type, energy) = line.split(" ", 1)
if bond_type.find("-") != -1:
(atom1, atom2) = bond_type.split("-")
order = 1
elif bond_type.find("=") != -1:
(atom1, atom2) = bond_type.split("=")
order = 2
elif bond_type.find("#") != -1:
(atom1, atom2) = bond_type.split("#")
order = 3
else:
raise ChemException("unable to parse bond type: " + bond_type)
bond_energy_table[(atom1, atom2, order)] = float(energy)
bond_energy_table[(atom2, atom1, order)] = float(energy)
def start(self, context, return_queue):
# Init script variables
start = time()
end = time()
website_driver = None
email_driver = None
logger = self.logger
disable_logging = self.disable_logging
exception_raised = True
exception_type = ""
proxy_address = ""
if BakecaSlave.use_proxy:
proxy_address = BakecaSlave.proxy.get_address()
if proxy_address is None:
raise ProxyException("No more proxies available!")
if BakecaSlave.use_lpm:
proxy_address = BakecaSlave.lpm_address
# Try and read last state from file
self.read_last_state()
# Get city and category and increment as needed
city_id, category_id = self.get_additional_data()
# Get image file and text file
self.parse_context(context)
logger.info("Parsed context %s." % str(context))
try:
# Get text from file
# Use up to 20 additional text_files with the same bot.
# BOT_TEXT_IMAGES/BAKECA/BAKECA_TEXT_FILE.txt
# BOT_TEXT_IMAGES/BAKECA/BAKECA_TEXT_FILE1.txt ... _FILE20.txt
text_file_list = [BakecaSlave.text_file]
for i in range(1, 21):
(basename, ext) = os.path.splitext(BakecaSlave.text_file)
i_text_file = basename + str(i) + ext
if os.path.exists(i_text_file):
text_file_list.append(i_text_file)
text_file_id = self.city_index % len(text_file_list)
text_file_x = text_file_list[text_file_id]
logger.info("Getting title and content from: %s" % text_file_x)
age, title, content = util.parse_text_file(text_file_x)
logger.info("Got title and content.")
# First go and get mail
email_driver = util.get_chrome_driver(BakecaSlave.is_headless,
proxy_address)
# util.go_to_page(driver=email_driver, page_url=util.MOAKT_URL)
# email = util.moakt_get_email_address(email_driver)
# email = util.smail_get_email_address(email_driver)
email = self.smailpro_man.get_email_address(self.slave_index)
password = util.random_string(10)
# Get images
logger.info("Got email [%s] and password [%s]" % (email, password))
images, out_message = util.get_images(BakecaSlave.image_dir)
logger.info(out_message)
# Go to Site
logger.info("Opening website page...")
website_driver = util.get_chrome_driver(BakecaSlave.is_headless,
proxy_address)
util.go_to_page(driver=website_driver,
page_url=CONSTANTS.WEBSITE_URL)
# Post without register
logger.info("Make website post...")
is_telg_auth, is_chiudi, loaded_images = self.make_website_post(
website_driver, city_id, category_id, age, title, content,
images, email)
# Close website driver
website_driver.quit()
# If not TELEGRAM Auth continue with post flow
if not is_telg_auth:
# Sleep for mail to arrive
sleep(5)
# Go to mail box
logger.info("Verify email...")
# util.moakt_access_verify_link(email_driver, '/html/body/p[5]/a')
# util.smail_validate_link(email_driver)
html_file = self.smailpro_man.get_message_as_temp_file()
email_driver.get("file://" + html_file)
sleep(2)
util.smailpro_validate_link(email_driver)
os.unlink(html_file)
# Click on accept
util.scroll_into_view_click_xpath(email_driver,
'//*[@id="accetto"]')
# Get post link
logger.info("Getting post url...")
announce_link = email_driver.find_element_by_xpath(
'//*[@id="colonna-unica"]/div[1]/p[1]/a')
post_url = announce_link.get_attribute('href')
# Close email driver
email_driver.quit()
print(post_url)
end = time()
exception_raised = False
except TimeoutException as e:
exception_type = "Timeout on page wait."
logger.exception("Timeout on page wait.")
raise BakecaException("Timeout on page wait.")
except NoSuchElementException as e:
exception_type = "Element not found."
logger.exception("Element not found.")
raise BakecaException("Element not found.")
except ElementNotInteractableException as e:
exception_type = "Element not interactable."
logger.exception("Element not interactable.")
raise BakecaException("Element not interactable.")
except util.UtilParseError as e:
exception_type = "Parse error."
logger.exception("Parse error.")
raise BakecaException("Parse error.")
except util.CaptchaSolverException as e:
exception_type = "Failed to solve captcha in time."
logger.exception("Failed to solve captcha in time.")
raise BakecaException("Failed to solve captcha in time.")
except TelegramAuthException as e:
exception_type = "TelegramAuth was required."
logger.exception("TelegramAuth was required.")
raise BakecaException("TelegramAuth was required.")
except SMailProException as e:
exception_type = "SMailPro exception occurred."
logger.exception("SMailPro exception occurred.")
raise BakecaException("SMailPro exception occurred.")
except BakecaException as e:
exception_type = "Bakeca exception occurred"
logger.exception("Bakeca exception occurred")
raise e
except Exception as e:
exception_type = "Unknown error."
logger.exception("Unknown error.")
raise BakecaException("Unknown error.")
finally:
# Close driver
if email_driver is not None:
email_driver.quit()
if website_driver is not None:
website_driver.quit()
if BakecaSlave.use_proxy:
BakecaSlave.proxy.set_valid(False)
BakecaSlave.proxy.__exit__(None, None, None)
self.write_last_state()
if exception_raised:
end = time()
logger.info(
"Exception was raised. Writing error to credentials.")
util.save_credentials_error(BAKECA_CREDENTIALS_PATH,
exception_type, "bakeca.com",
CONSTANTS.CITIES[city_id],
CONSTANTS.CATEGORIES[category_id],
end - start,
BakecaSlave.bakeca_lock)
announce_msg = (
"BAKECA !!!FAILED!!! For City %s and category %s." %
(CONSTANTS.CITIES[city_id],
CONSTANTS.CATEGORIES[category_id]))
logger.info(announce_msg)
print(announce_msg)
self.push_to_fail_queue(city_id, category_id)
bot_logger.close_logger(logger, disable_logging)
# if failed to solve captcha simply retry
if exception_type is "Failed to solve captcha in time.":
return_queue.put(BAKECA_RETRY)
return BAKECA_RETRY
else:
return_queue.put(BAKECA_ERROR)
return BAKECA_ERROR
# Success - save credentials and post url
website = "bakeca.com" + "\n" + "City: " + CONSTANTS.CITIES[city_id] + "\n" + "Category: " + \
CONSTANTS.CATEGORIES[category_id] + "\n" + "Is chiudi: " + str(
is_chiudi) + "\n" + "Images loaded: " + str(loaded_images)
if is_telg_auth:
util.save_credentials(BAKECA_CREDENTIALS_PATH, email, password,
"FAILED - TELEGRAM AUTH REQUIRED", website,
end - start, BakecaSlave.bakeca_lock)
# The telegram banner blocked the posting. Leave it and switch the city_it.
announce_msg = (
"BAKECA !!!FAILED-TELEGRAM!!! For City %s and category %s." %
(CONSTANTS.CITIES[city_id], CONSTANTS.CATEGORIES[category_id]))
print(announce_msg)
logger.info(announce_msg)
else:
util.save_credentials(BAKECA_CREDENTIALS_PATH, email, password,
post_url, website, end - start,
BakecaSlave.bakeca_lock)
# Post succeeded.
announce_msg = (
"BAKECA Success For City %s and category %s." %
(CONSTANTS.CITIES[city_id], CONSTANTS.CATEGORIES[category_id]))
print(announce_msg)
logger.info(announce_msg)
bot_logger.close_logger(logger, disable_logging)
return_queue.put(BAKECA_SUCCESS)
if BakecaSlave.use_proxy:
BakecaSlave.proxy.set_valid(True)
BakecaSlave.proxy.__exit__(None, None, None)
return BAKECA_SUCCESS
#!/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()
#!/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 init():
global params; params = {}
params['MPL'] = 3 # Maximal Path Length. Set to None to restrict all options to have minimal length.
params['ID'] = False # Isomerase Down. True will prevent Isomerases from acting on phosphorylated compounds.
params['EA'] = False # Epimerase Above. True will prevent Epimerases from acting on unphosphorylated compounds.
params['PECK'] = True # Pentose Epimerases Can only work on Ketoses.
params['3ECK'] = False # 3-Epimerases Can only work on Ketoses.
params['TL'] = 1 # Total Length.
params['NE'] = None # Number of Epimerases (counts isoenzymes twice)
params['NI'] = None # Number of Isomerases (counts isoenzymes twice)
params['ND'] = None # Number of Dehydrogenases (counts isoenzymes twice)
params['NK'] = None # Number of Kinases (counts isoenzymes twice)
params['NTE'] = 0 # No Two Epimerases. Number maximum number of same-product epimerases.
params['MIE'] = None # Counts the number of isoenzyme pairs.
params['TPD'] = None # Total Phosphorylation Distance. Counts the total number of steps before phosphorylation.
for p in params.keys():
print "%s = %s" % (p, str(params[p]))
global enzyme_types, target, sources, pp_enzymes
# this is the target if the metabolism (in our case the input to the PP cycle)
target = 'D-Ribulose-5P'
# sources are sugars that E. coli can grow on without any other carbon source
sources = ['Ribitol', 'D-Arabitol', 'L-Xylulose', 'D-Ribose', 'D-Xylose', 'L-Arabinose']
ketoses = ['D-Ribulose', 'L-Ribulose', 'D-Xylulose', 'L-Xylulose']
aldoses = ['D-Ribose', 'L-Ribose', 'D-Arabinose', 'L-Arabinose', 'D-Xylose', 'L-Xylose', 'D-Lyxose', 'L-Lyxose']
polyols = ['Ribitol', 'Xylitol', 'D-Arabitol', 'L-Arabitol']
# these are edges that don't have a cost since they are part of the PP cycle
pp_enzymes = [('D-Ribulose-5P', 'D-Ribose-5P'), ('D-Ribulose-5P', 'D-Xylulose-5P')]
pp_enzymes += [(j, i) for (i, j) in pp_enzymes]
global G_pentoses, G_wildtype, G_conjecture
G_wildtype = SparseGraph()
G_conjecture = SparseGraph()
G_pentoses = SparseGraph()
# Add all the known possible enzymes to G_pentoses
enzyme_types = {}
for line in parse_text_file("../rec/pentoses_edges.txt"):
tokens = line.split()
for i in [0, 1]:
compound = tokens[i]
neighbor = tokens[1-i]
(enzyme_type, i_carbon) = tokens[2].split('-')
# add the kinases
G_pentoses[compound][compound + '-5P'] = 1
enzyme_types[(compound, compound + '-5P')] = "KIN"
G_pentoses[compound + '-5P'][compound] = 1
enzyme_types[(compound + '-5P', compound)] = "KIN"
if (params['EA'] and (enzyme_type == "EPI")):
pass # in EA mode don't use Epimerases on phosphorylated forms
elif (params['PECK'] and (enzyme_type == "EPI") and (not compound in ketoses)):
pass # in PECK mode don't use Epimerases on non-ketoses
elif (params['3ECK'] and (enzyme_type == "EPI") and (i_carbon == 3) and (not compound in ketoses)):
pass # in 3ECK mode don't use 3-Epimerases on non-ketoses
else:
G_pentoses[compound][neighbor] = 1
enzyme_types[(compound, neighbor)] = enzyme_type
if (params['ID'] and (enzyme_type == "DHG")):
pass # in ID mode don't use Dehydrogenases on phosphorylated forms
elif (params['PECK'] and (enzyme_type =="EPI") and (not compound in ketoses)):
pass # in PECK mode don't use Epimerases on non-ketoses
elif (params['3ECK'] and (enzyme_type == "EPI") and (i_carbon == 3) and (not compound in ketoses)):
pass # in 3ECK mode don't use 3-Epimerases on non-ketoses
else:
G_pentoses[compound + '-5P'][neighbor + '-5P'] = 1
enzyme_types[(compound + '-5P', neighbor + '-5P')] = enzyme_type
# Change the cost of the PP enzymes to 0
for (i, j) in pp_enzymes:
G_wildtype[i][j] = 0
G_pentoses[i][j] = 0
G_conjecture[i][j] = 0
# these are edges that do exist in E. coli but are not part of the Pentose Phosphate Cycle
# according to KEGG and MetaCyc
wildtype_enzymes = \
[('D-Arabitol', 'D-Xylulose'),\
('D-Xylulose', 'D-Xylulose-5P'),\
('D-Xylose', 'D-Xylulose'),\
('L-Arabinose', 'L-Ribulose'),\
('L-Ribulose', 'L-Ribulose-5P'),\
('L-Xylulose', 'L-Xylulose-5P'),\
('L-Xylulose-5P','L-Ribulose-5P'),\
('L-Ribulose-5P','D-Xylulose-5P'),\
('Ribitol', 'D-Ribulose'),\
('D-Ribulose', 'D-Ribulose-5P'),\
('D-Ribose', 'D-Ribose-5P')]
for (i, j) in wildtype_enzymes:
G_wildtype[i][j] = 1
G_wildtype[j][i] = 1
# these is the conjectured list of non-PP enzymes (where the L-Xylulose path is shorter)
conjecture_enzymes = \
[('D-Arabitol', 'D-Xylulose'),\
('D-Xylulose', 'D-Xylulose-5P'),\
('D-Xylose', 'D-Xylulose'),\
('L-Arabinose', 'L-Ribulose'),\
('L-Ribulose', 'L-Ribulose-5P'),\
('L-Xylulose', 'L-Xylulose-5P'),\
('L-Xylulose-5P','D-Ribulose-5P'),\
('L-Ribulose-5P','D-Xylulose-5P'),\
('Ribitol', 'D-Ribulose'),\
('D-Ribulose', 'D-Ribulose-5P'),\
('D-Ribose', 'D-Ribose-5P')]
for (i, j) in conjecture_enzymes:
G_conjecture[i][j] = 1
G_conjecture[j][i] = 1
html_path = "../pathways/html"
#pathway = "glycolysis-2"
pathway = "glycolysis-1"
#pathway = "pentose-phosphate"
#pathway = "frucolysis"
util._mkdir(html_path + "/" + pathway)
html_filename = html_path + "/" + pathway + ".html"
html_file = open(html_filename, "w")
prev_line_bag = None
reaction_titles = []
reaction_compounds = []
line_number = 0
for line in util.parse_text_file(pathway_path + "/" + pathway + ".pth"):
line_number += 1
if (line[0:2] == "//"):
prev_line_bag = None
reaction_titles.append("*"*60 + " " + line[2:] + " " + "*"*60)
reaction_compounds.append(None)
elif (prev_line_bag == None):
prev_line_bag = bag.Bag().from_string(line)
else:
curr_line_bag = bag.Bag().from_string(line)
common_bag = curr_line_bag.intersection(prev_line_bag)
side_bags = [prev_line_bag - common_bag, curr_line_bag - common_bag, common_bag] # left-side, right-side, common
side_strings = ["", "", ""]
side_graphs = (ChemGraph(), ChemGraph(), ChemGraph())
for side in range(3):
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()
