def __init__(self, db, public_db, html_writer,
thermo=None,
thermodynamic_method='global',
max_reactions=None,
max_solutions=100,
maximal_dG=0.0,
update_file=None,
output_kegg_file=None):
"""Initialize the Pathologic object.
Args:
db: the DB to read group contribution data from.
html_writer: an HtmlWriter for writing output.
thermodynamic_method: the analysis methods.
Options are: "none", "pCr", "MTDF", "global" or "localized"
max_reactions: the maximum number of reactions to find in a solution (use None for unlimited)
max_solutions: the maximum number of solutions to find (use None for unlimited)
maximal_dG: the maximum dG allowed.
Use this to change the thermodynamic constraints to have a different
MTDF. When set to 0, it is the usual feasibility measure.
update_file: the file to read for KEGG updates.
"""
assert thermodynamic_method in OptimizationMethods.ALLOWED_METHODS
util._mkdir('../res/pathologic')
self.html_writer = html_writer
self.thermodynamic_method = thermodynamic_method
self.max_reactions = max_reactions
self.max_solutions = max_solutions
self.maximal_dG = maximal_dG
self.db_public = public_db
self.db = db
self.thermo = thermo
self.kegg_pathologic = KeggPathologic()
if update_file is not None:
self.kegg_pathologic.update_database(update_file, self.html_writer)
class Pathologic(object):
def __init__(self, db, public_db, html_writer,
thermo=None,
thermodynamic_method='global',
max_reactions=None,
max_solutions=100,
maximal_dG=0.0,
update_file=None,
output_kegg_file=None):
"""Initialize the Pathologic object.
Args:
db: the DB to read group contribution data from.
html_writer: an HtmlWriter for writing output.
thermodynamic_method: the analysis methods.
Options are: "none", "pCr", "MTDF", "global" or "localized"
max_reactions: the maximum number of reactions to find in a solution (use None for unlimited)
max_solutions: the maximum number of solutions to find (use None for unlimited)
maximal_dG: the maximum dG allowed.
Use this to change the thermodynamic constraints to have a different
MTDF. When set to 0, it is the usual feasibility measure.
update_file: the file to read for KEGG updates.
"""
assert thermodynamic_method in OptimizationMethods.ALLOWED_METHODS
util._mkdir('../res/pathologic')
self.html_writer = html_writer
self.thermodynamic_method = thermodynamic_method
self.max_reactions = max_reactions
self.max_solutions = max_solutions
self.maximal_dG = maximal_dG
self.db_public = public_db
self.db = db
self.thermo = thermo
self.kegg_pathologic = KeggPathologic()
if update_file is not None:
self.kegg_pathologic.update_database(update_file, self.html_writer)
def add_reaction(self, reaction, weight=1.0):
self.kegg_pathologic.add_reaction(reaction, weight)
def delete_reaction(self, rid):
self.kegg_pathologic.delete_reaction(rid)
def add_cofactor_reaction(self, reaction):
self.kegg_pathologic.add_cofactor_reaction(reaction)
def ban_compound(self, cid):
"""Deletes all reactions which involve this compounds as a reactant"""
self.kegg_pathologic.banned_cids.add(cid)
def find_path(self, experiment_name, net_reaction):
"""Find a pathway from the source to the target.
Args:
experiment_name: a name given to this experiment.
net_reaction: a Reaction describing the net reaction for the desired paths
"""
dirname = os.path.join('../res/pathologic/', experiment_name)
logging.info('Writing output to: %s' % dirname)
util._mkdir(dirname)
self.html_writer.write('<a href="pathologic/' + experiment_name + '.html">' + experiment_name + '</a><br>\n')
exp_html = HtmlWriter('../res/pathologic/' + experiment_name + '.html')
exp_html.write("<p><h1>%s</h1>\n" % experiment_name)
exp_html.insert_toggle(div_id="__parameters__", start_here=True,
label='Show Parameters')
f, S, compounds, reactions = self.kegg_pathologic.get_unique_cids_and_reactions()
exp_html.write('<h2>Conditions:</h2>\n')
exp_html.write_ul(['Optimization method: %s' % self.thermodynamic_method,
'Concentration range: %g M < C < %g M' % (self.thermo.c_range[0], self.thermo.c_range[1]),
"Max Δ<sub>r</sub>G' = %.1f" % self.maximal_dG,
'pH = %g' % self.thermo.pH,
'I = %g' % self.thermo.I,
'T = %g' % self.thermo.T,
'Max no. reactions: %d' % (self.max_reactions or -1),
'Max no. solutions: %d' % (self.max_solutions or -1),
'Overall Reaction: %s' % net_reaction.to_hypertext(),
'%d reactions' % len(reactions),
'%d unique compounds' % len(compounds)])
exp_html.div_end()
exp_html.write('</br>\n')
logging.debug("All compounds:")
for i, compound in enumerate(compounds):
logging.debug("%05d) C%05d = %s" % (i, compound.cid, compound.name))
logging.debug("All reactions:")
for i, reaction in enumerate(reactions):
logging.debug("%05d) R%05d = %s" % (i, reaction.rid, str(reaction)))
output_kegg_file = open(dirname + '/kegg_pathway.txt', 'w')
exp_html.write('<a href="%s/kegg_pathway.txt">All solutions in KEGG format</a></br>\n'
% experiment_name)
# Find a solution with a minimal total flux
logging.info("Preparing LP solver for the minimal total flux problem")
exp_html.write('<b>Minimum flux</b>')
slip = Stoichiometric_LP("Pathologic")
slip.add_stoichiometric_constraints(f, S, compounds, reactions, net_reaction)
slip.export("../res/pathologic/%s/%03d_lp.txt" % (experiment_name, 0))
exp_html.write(' (<a href="%s/%03d_lp.txt">LP file</a>): ' % (experiment_name, 0))
logging.info("Solving")
if not slip.solve():
exp_html.write("<b>There are no solutions!</b>")
logging.warning("There are no solutions. Quitting!")
return
logging.info("writing solution")
self.write_current_solution(exp_html, slip, experiment_name)
logging.info("Preparing MILP solver")
milp = Stoichiometric_LP("Pathologic")
milp.solution_index = 1
milp.add_stoichiometric_constraints(f, S, compounds, reactions, net_reaction)
milp.add_milp_variables()
if self.max_reactions is not None:
milp.add_reaction_num_constraint(self.max_reactions)
if self.thermodynamic_method == OptimizationMethods.LOCALIZED:
milp.add_localized_dGf_constraints(self.thermo)
else:
milp.add_dGr_constraints(self.thermo,
optimization=self.thermodynamic_method,
maximal_dG=self.maximal_dG)
index = 0
while (self.max_solutions is None) or (index < self.max_solutions):
index += 1
# create the MILP problem to constrain the previous solutions not to reappear again.
logging.info("Round %03d, solving using MILP" % (milp.solution_index))
milp.export("../res/pathologic/%s/%03d_lp.txt" % (experiment_name, milp.solution_index))
exp_html.write('<b>Solution #%d</b> (<a href="%s/%03d_lp.txt">LP file</a>): ' % (index, experiment_name, index))
if not milp.solve():
exp_html.write("<b>No solution found</b>")
logging.info("No more solutions. Quitting!")
break
logging.info("writing solution")
self.write_current_solution(exp_html, milp, experiment_name,
output_kegg_file)
milp.ban_current_solution()
output_kegg_file.close()
exp_html.close()
def write_current_solution(self, exp_html, lp, experiment_name,
output_kegg_file=None):
solution = lp.get_active_reaction_data()
solution_id = '%03d' % lp.solution_index
exp_html.write('%d reactions, flux = %g, \n' %
(len(solution.reactions),
float(solution.fluxes.sum(1))))
# draw network as a graph and link to it
Gdot = self.kegg_pathologic.draw_pathway(solution.reactions,
list(solution.fluxes.flat))
svg_fname = '%s/%s_graph' % (experiment_name, solution_id)
exp_html.embed_dot_inline(Gdot, width=240, height=320, name=svg_fname)
# write the solution for the concentrations in a table
if solution.concentrations is not None:
exp_html.insert_toggle(start_here=True)
rowdicts = []
for c, compound in enumerate(solution.compounds):
rowdict = {}
rowdict['KEGG ID'] = '<a href="%s">C%05d</a>' % (compound.get_link(), compound.cid)
rowdict['Compound'] = compound.name
rowdict[symbol_df_G0_prime + "[kJ/mol]"] = solution.dG0_f[0, c]
rowdict[symbol_df_G_prime + "[kJ/mol]"] = solution.dG_f[0, c]
if np.isfinite(solution.concentrations[0, c]):
rowdict['Conc. [M]'] = '%.1e' % solution.concentrations[0, c]
else:
rowdict['Conc. [M]'] = 'N/A'
rowdicts.append(rowdict)
headers=['KEGG ID', 'Compound', symbol_df_G0_prime + "[kJ/mol]",
symbol_df_G_prime + "[kJ/mol]", 'Conc. [M]']
exp_html.write('Compound Concentrations<br>\n')
exp_html.write_table(rowdicts, headers, decimal=1)
total_reaction = Reaction('total', {})
rowdicts = []
for r, reaction in enumerate(solution.reactions):
rowdict = {}
flux = solution.fluxes[0, r]
rowdict['KEGG ID'] = '<a href="%s">R%05d</a>' % (reaction.get_link(), reaction.rid)
rowdict['Reaction'] = reaction.to_hypertext(show_cids=False)
rowdict['Flux'] = flux
rowdict[symbol_dr_Gc_prime + "[kJ/mol]"] = solution.dGc_r[0, r]
rowdict[symbol_dr_G_prime + "[kJ/mol]"] = solution.dG_r[0, r]
rowdicts.append(rowdict)
total_reaction += (flux * reaction)
rowdict = {}
rowdict['KEGG ID'] = 'total'
rowdict['Reaction'] = total_reaction.to_hypertext(show_cids=False)
rowdict[symbol_dr_Gc_prime + "[kJ/mol]"] = float(solution.dGc_r.sum(1))
rowdict[symbol_dr_G_prime + "[kJ/mol]"] = float(solution.dG_r.sum(1))
rowdicts.append(rowdict)
headers=['KEGG ID', 'Reaction', symbol_dr_Gc_prime + "[kJ/mol]",
symbol_dr_G_prime + "[kJ/mol]", "Flux"]
exp_html.write('Reaction Gibbs energies<br>\n')
exp_html.write_table(rowdicts, headers, decimal=1)
exp_html.div_end()
# write the pathway in KEGG format
if output_kegg_file is not None:
write_kegg_pathway(output_kegg_file,
entry=experiment_name + ' ' + solution_id,
reactions=solution.reactions,
fluxes=list(solution.fluxes.flat))
exp_html.write('<br>\n')