def python_main():
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
from python.component_contribution import ComponentContribution
from python.kegg_model import KeggModel
from python.thermodynamic_constants import default_RT
cc = ComponentContribution.init()
reaction_strings = open(REACTION_FNAME, 'r').readlines()
model = KeggModel.from_formulas(reaction_strings)
model.add_thermo(cc)
dG0_prime, dG0_std = model.get_transformed_dG0(7.0, 0.2, 298.15)
cid2c_range = {cid : (1e-6, 1e-2) for cid in model.cids}
cid2c_range['C00001'] = (1.0, 1.0) # water must be always set to 1 (equivalent to 55M)
ln_conc = np.log(np.matrix([cid2c_range[cid] for cid in model.cids]))
ln_conc_min = ln_conc[:, 0]
ln_conc_max = ln_conc[:, 1]
S_minus, S_plus = model.get_unidirectional_S()
dG_prime_min = dG0_prime + default_RT * (S_minus.T * ln_conc_max + S_plus.T * ln_conc_min)
dG_prime_max = dG0_prime + default_RT * (S_minus.T * ln_conc_min + S_plus.T * ln_conc_max)
for i, r in enumerate(reaction_strings):
print '-'*50
print r.strip()
print "dG'0 = %8.1f +- %5.1f" % (dG0_prime[i, 0], dG0_std[i, i] * 1.96)
print "dG' range = %8.1f - %8.1f" % (dG_prime_min[i, 0], dG_prime_max[i, 0])
if dG_prime_min[i, 0] < 0 and dG_prime_max[i, 0] > 0:
print "REVERSIBLE!"
else:
print "IRREVERSIBLE!"
def main():
td = TrainingData()
cc = ComponentContribution.init()
G1 = np.matrix(cc.params['G1'])
G2 = np.matrix(cc.params['G2'])
G3 = np.matrix(cc.params['G3'])
############################################################################
#reaction = KeggReaction.parse_formula('C00002 + C00001 <=> C00008 + C00009'); fname = 'atpase';
reaction = KeggReaction.parse_formula('C00149 <=> C00122 + C00001'); fname = 'fumarase';
x, g = cc._decompose_reaction(reaction)
weights_rc = (x.T * G1).round(5)
weights_gc = (x.T * G2 + g.T * G3).round(5)
weights = weights_rc + weights_gc
orders = sorted(range(weights.shape[1]),
key=lambda j:abs(weights[0, j]), reverse=True)
output = csv.writer(open('res/%s_analysis.csv' % fname, 'w'))
output.writerow(('Weight', 'dG\'0', 'dG0', 'reference', 'reaction'))
for j in orders:
if abs(weights[0, j]) < 1e-7:
continue
output.writerow((weights_rc[0, j], td.dG0_prime[j], td.dG0[j],
td.reference[j], td.description[j]))
def python_main():
logger = logging.getLogger("")
logger.setLevel(logging.INFO)
from python.component_contribution import ComponentContribution
from python.kegg_model import KeggModel
cc = ComponentContribution.init()
reaction_strings = open(REACTION_FNAME, "r").readlines()
model = KeggModel.from_formulas(reaction_strings)
model.add_thermo(cc)
dG0_prime, dG0_std = model.get_transformed_dG0(7.0, 0.2, 298.15)
print "For a linear problem, define two vector variables 'x' and 'y', each of length Nr (i.e. " + "the same length as the list of reactions). Then add these following " + "constraints: \n" + "-1 <= y <= 1\n" + "x = dG0_prime + 3 * dG0_std * y\n" + "Then use 'x' as the value of the standard Gibbs energy for all reactions."
print "The results are writteng to: " + OUTPUT_FNAME
savemat(OUTPUT_FNAME, {"dG0_prime": dG0_prime, "dG0_std": dG0_std}, oned_as="row")
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 23 14:58:44 2014
@author: eladn
"""
from python.component_contribution import ComponentContribution
from python.kegg_reaction import KeggReaction
#pH = 7
I = 0.2
T = 298.15
F = 96.48 / 1000.0 # kJ/mol / mV
cc = ComponentContribution.init()
#formula = 'C00033 + C00282 <=> C00084 + C00001'
#formula = 'C00067 + C00001 <=> C00058 + C00010'
formula = 'C00003 + C00282 <=> C00004'
#############################################################################
reaction = KeggReaction.parse_formula(formula)
reaction_atom_bag = reaction._get_reaction_atom_bag()
n_e = 0
if 'e-' in reaction_atom_bag:
n_e = reaction_atom_bag['e-']
del reaction_atom_bag['e-']
if len(reaction_atom_bag) != 0:
raise Exception('Reaction is not balanced (not only with respect to e-)')
dG0_r, u_r = cc.get_dG0_r(reaction)
for pH in xrange(6, 9):
import csv, os
import numpy as np
import matplotlib.pyplot as plt
from python.component_contribution import ComponentContribution
from python.kegg_model import KeggModel
REPORT_CACHE_FNAME = 'cache/report_gc.csv'
cid2dG0 = {}
if not os.path.exists(REPORT_CACHE_FNAME):
fp = open(REPORT_CACHE_FNAME, 'w')
cc = ComponentContribution()
cc.train()
csv_out = csv.writer(fp)
csv_out.writerow(['cid', 'dG0_f'])
for compound_id in cc.ccache.get_all_compound_ids():
dG0_f = cc.get_major_ms_dG0_f(compound_id)
csv_out.writerow([compound_id, '%8.2f' % dG0_f])
cid2dG0[compound_id] = dG0_f
else:
for row in csv.DictReader(open(REPORT_CACHE_FNAME, 'r')):
cid2dG0[row['cid']] = float(row['dG0_f'])
REACTION_FNAME = 'tests/report_gc_reactions.txt'
reaction_strings = open(REACTION_FNAME, 'r').readlines()
model = KeggModel.from_formulas(reaction_strings)
# compare the dG0_r of the model to the one we get if multiplying
# the model stoichiometric matrix by the formation energies
model_dG0_f = np.matrix([cid2dG0[cid] for cid in model.cids]).T
model_dG0_r = model.S.T * model_dG0_f
