from nist import Nist
import pylab
from alberty import Alberty, MissingCompoundFormationEnergy
from hatzimanikatis import Hatzi
from groups import GroupContribution
from thermodynamic_constants import R
import logging
import sys
from toolbox.database import SqliteDatabase
from pygibbs.kegg_reaction import Reaction
A = Alberty()
H = Hatzi()
db = SqliteDatabase('../res/gibbs.sqlite')
gc = GroupContribution(db)
gc.init()
nist = Nist()
def pH_dependence():
analyze_this_reaction = []
I_mid = []
I_tolerance = []
T_mid = []
T_tolerance = []
analyze_this_reaction += [Reaction(['glucose kinase'], {2:-1, 31:-1, 8:1, 92:1})]
I_mid += [0.01]
I_tolerance += [0.02]
T_mid += [303.1]
T_tolerance += [0.1]
from groups import GroupContribution
from pygibbs.thermodynamic_constants import R
from pylab import log, zeros, pinv, dot, plot, show, figure, NaN, isnan, find
import csv
from pygibbs.kegg_utils import unparse_reaction_formula
from pygibbs.kegg_reaction import Reaction
G = GroupContribution(sqlite_name="gibbs.sqlite", html_name="acetogens")
G.init()
reactions = [] # (RID, EC, sparse-reaction, dG0_r, pH, I, T
# Drake 2006
#reactions.append([134, '1.2.1.43', {11:-1, 5:-1, 58:1, 6:1}, 22, 7.0, 0, 300])
reactions.append([
934, '6.3.4.3', {
101: -1,
58: -1,
2: -1,
8: 1,
9: 1,
234: 1
}, -8, 7.0, 0, 300
])
reactions.append([1655, '3.5.4.9', {234: -1, 445: 1, 1: 1}, -4, 7.0, 0, 300])
reactions.append(
[1220, '1.5.1.5', {
445: -1,
5: -1,
143: 1,
6: 1
for n in range(n_begin, N):
(sparse_reaction, pH, I, T, evaluation, dG0_obs) = grad.data[n]
n_measurements = min(
[nist.cid2count[cid] for cid in sparse_reaction.keys()])
reaction_str = gc.kegg().sparse_reaction_to_string(sparse_reaction,
cids=True)
dG0_est = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
csv_results.writerow([
n, dG0_obs, dG0_est, reaction_str, pH, I, T, evaluation,
n_measurements
])
res_file.flush()
################################################################################
if (len(sys.argv) > 1):
n_begin = int(sys.argv[1])
else:
n_begin = 0
gc = GroupContribution(sqlite_name="gibbs.sqlite", html_name="dG0_test")
gc.init()
nist = Nist(gc.kegg())
alberty = Alberty()
sensitivity_analysis_for_gradient_ascent(gc,
nist,
alberty.cid2pmap_dict,
max_i=250,
n_begin=n_begin)
#evaluate(gc, nist, alberty.cid2pmap_dict)
grad = GradientAscent(gc)
grad.cid2pmap_dict = deepcopy(cid2pmap)
grad.load_nist_data(nist, skip_missing_reactions=True)
res_file = open('../res/evaluation_report.csv', 'w')
csv_results = csv.writer(res_file)
csv_results.writerow(["N", "dG0_obs", "dG0_est", "reaction", "pH", "I", "T", "evaluation"])
N = len(grad.data)
for n in range(n_begin, N):
(sparse_reaction, pH, I, T, evaluation, dG0_obs) = grad.data[n]
n_measurements = min([nist.cid2count[cid] for cid in sparse_reaction.keys()])
reaction_str = gc.kegg().sparse_reaction_to_string(sparse_reaction, cids=True)
dG0_est = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
csv_results.writerow([n, dG0_obs, dG0_est, reaction_str, pH, I, T, evaluation, n_measurements])
res_file.flush()
################################################################################
if (len(sys.argv) > 1):
n_begin = int(sys.argv[1])
else:
n_begin = 0
gc = GroupContribution(sqlite_name="gibbs.sqlite", html_name="dG0_test")
gc.init()
nist = Nist(gc.kegg())
alberty = Alberty()
sensitivity_analysis_for_gradient_ascent(gc, nist, alberty.cid2pmap_dict, max_i=250, n_begin=n_begin)
#evaluate(gc, nist, alberty.cid2pmap_dict)
from groups import GroupContribution
from pygibbs.thermodynamic_constants import R
from pylab import log, zeros, pinv, dot, plot, show, figure, NaN, isnan, find
import csv
from pygibbs.kegg_utils import unparse_reaction_formula
from pygibbs.kegg_reaction import Reaction
G = GroupContribution(sqlite_name="gibbs.sqlite", html_name="acetogens")
G.init()
reactions = [] # (RID, EC, sparse-reaction, dG0_r, pH, I, T
# Drake 2006
#reactions.append([134, '1.2.1.43', {11:-1, 5:-1, 58:1, 6:1}, 22, 7.0, 0, 300])
reactions.append([934, '6.3.4.3', {101:-1, 58:-1, 2:-1, 8:1, 9:1, 234:1}, -8, 7.0, 0, 300])
reactions.append([1655, '3.5.4.9', {234:-1, 445:1, 1:1}, -4, 7.0, 0, 300])
reactions.append([1220, '1.5.1.5', {445:-1, 5:-1, 143:1, 6:1}, -5, 7.0, 0, 300])
reactions.append([1224, '1.5.1.20', {143:-1, 5:-1, 440:1, 6:1}, -22, 7.0, 0, 300])
# NIST database
reactions.append([134, '1.2.1.43', {288:1, 5:1, 58:-1, 6:-1, 1:-1}, -R * 328 * log(650), 7.5, 0, 328]) # Yamamoto 1983, Buffer: triethanolamine-maleate (0.1 M)
reactions.append([934, '6.3.4.3', {101:-1, 58:-1, 2:-1, 8:1, 9:1, 234:1}, -R * 310 * log(41), 7.7, 0, 310]) # Himes 1962, Buffer: triethanolamine (0.1 M)
reactions.append([1655, '3.5.4.9', {234:1, 445:-1, 1:-1}, -R * 298 * log(11), 7.0, 0, 298]) # Kay 1960, Buffer: acetate
reactions.append([1655, '3.5.4.9', {234:1, 445:-1, 1:-1}, -R * 298 * log(1.84), 6.5, 0, 298]) # Lombrozo 1967, Buffer: potassium citrate (0.11 M)
reactions.append([1655, '3.5.4.9', {234:1, 445:-1, 1:-1}, -R * 298 * log(4.2), 6.5, 0, 298]) # Greenberg 1963, Buffer: potassium maleate (1.0 M)
reactions.append([1655, '3.5.4.9', {234:1, 445:-1, 1:-1}, -R * 298 * log(50), 7.0, 0, 298]) # Suzuki 1973, Buffer: potassium maleate
reactions.append([1220, '1.5.1.5', {445:1, 5:1, 143:-1, 6:-1}, -R * 298 * log(0.14), 6.9, 0, 298]) # Uyeda 1967, Buffer: potassium maleate (0.05 M)
reactions.append([1220, '1.5.1.5', {445:1, 5:1, 143:-1, 6:-1}, -R * 303 * log(16), 7.3, 0, 303]) # Pelletier 1995, Buffer: potassium phosphate (0.025 M)
reactions.append([8550, '1.8.1.4', {2972:-1, 3:-1, 2051:1, 4:1}, -R * 295 * log(0.21), 7.1, 0, 295]) # Sanadi 1957, Buffer: phosphate (0.026 M)
reactions.append([8550, '1.8.1.4', {2972:-1, 3:-1, 2051:1, 4:1}, -R * 295 * log(0.13), 7.1, 0, 295]) # Sanadi 1959, Buffer: phosphate (0.05 M)
reactions.append([945, '2.1.2.1', {143:-1, 37:-1, 1:-1, 101:1, 65:1}, -R * 310 * log(0.067), 7.4, 0, 310]) # Besson 1993, Buffer: KH2PO4 (0.02 M)