...
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Commits (5)
......@@ -35,19 +35,18 @@ The easiest way to get eQuilibrator-API up and running is using virtualenv, PyPI
```
virtualenv -p python3 equilibrator
source equilibrator/bin/activate
pip install equilibrator-api jupyter
curl https://gitlab.com/elad.noor/equilibrator-api/raw/develop/scripts/equilibrator_cmd.ipynb > equilibrator_cmd.ipynb
jupyter notebook
pip install equilibrator-api
```
Then select the notebook called `equilibrator_cmd.ipynb` and follow the examples in it.
You can then either follow the examples below and write your own code, or start by opening the
provided jupyter notebook:
Alternatively, you could install from source. Make sure you have [git-lfs](https://git-lfs.github.com/)
installed before cloning the repository:
```
git clone https://gitlab.com/elad.noor/equilibrator-api.git
cd equilibrator-api
python setup.py install
```
pip install jupyter
curl https://gitlab.com/elad.noor/equilibrator-api/raw/develop/scripts/equilibrator_cmd.ipynb > equilibrator_cmd.ipynb
jupyter notebook
```
Then select the notebook called `equilibrator_cmd.ipynb` and follow the examples in it.
## Example Usage
......@@ -63,7 +62,7 @@ You can parse a reaction from a KEGG-style reaction string. The example given
is ATP hydrolysis to ADP and inorganic phosphate.
```python
rxn_str = "KEGG:C00002 + KEGG:C00001 = KEGG:C00008 + KEGG:C00009"
rxn_str = "kegg:C00002 + kegg:C00001 = kegg:C00008 + kegg:C00009"
rxn = Reaction.parse_formula(rxn_str)
```
......@@ -84,14 +83,14 @@ calculate the standard change in Gibbs potential due to this reaction.
# You control the pH and ionic strength!
# ionic strength is in Molar units.
standard_dg_prime, uncertainty = eq_api.standard_dg_prime(rxn)
print("dG0' = %s \u00B1 %s\n" % (standard_dg_prime, uncertainty))
print(f"dG0' = {standard_dg_prime:.1f} \u00B1 {uncertainty:1.f}")
```
You can also calculate the [reversibility index](https://doi.org/10.1093/bioinformatics/bts317) for this reaction.
```python
ln_RI = eq_api.ln_reversibility_index(rxn)
print('ln(Reversibility Index) = %s\n' % ln_RI)
print(f"ln(Reversibility Index) = {ln_RI:.1f}")
```
The reversibility index is a measure of the degree of the reversibility of the
......
......@@ -37,8 +37,7 @@ import pandas as pd
def main(args):
"""Run main script, calculates the reaction Gibbs energy change."""
from equilibrator_api import (
Q_, ComponentContribution, Reaction, ureg) # isort:skip
ureg.default_format = ".2f"
Q_, ComponentContribution, Reaction) # isort:skip
p_h = Q_(args.ph)
assert p_h.check(None)
......@@ -115,3 +114,4 @@ if __name__ == '__main__':
logging.getLogger().setLevel(logging.WARNING)
args = parser.parse_args()
main(args)
......@@ -16,7 +16,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"MNXM2 + MNXM3 = MNXM7 + MNXM9\n",
"MNXM2(l) + MNXM3(aq) = MNXM7(aq) + MNXM9(aq)\n",
"ΔG'° = -25.8 kilojoule / mole ± 0.6 kilojoule / mole\n",
"ΔG'm = -42.9 kilojoule / mole ± 0.6 kilojoule / mole\n",
"ΔG' = -45.9 kilojoule / mole ± 0.6 kilojoule / mole\n"
......@@ -100,65 +100,65 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MNXM2</td>\n",
" <td>MNXM2(l)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.5</td>\n",
" <td>5.000000e-01</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MNXM5</td>\n",
" <td>MNXM5(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.5</td>\n",
" <td>5.000000e-01</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MNXM6</td>\n",
" <td>MNXM6(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>-0.5</td>\n",
" <td>-5.000000e-01</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MNXM13</td>\n",
" <td>MNXM13(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MNXM145</td>\n",
" <td>MNXM145(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.0</td>\n",
" <td>1.110223e-16</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>MNXM160</td>\n",
" <td>MNXM160(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.5</td>\n",
" <td>5.000000e-01</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>MNXM325</td>\n",
" <td>MNXM325(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>-0.5</td>\n",
" <td>-5.000000e-01</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>MNXM429</td>\n",
" <td>MNXM429(aq)</td>\n",
" <td>0.0 molar</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>100.0 micromolar</td>\n",
" <td>10.0 millimolar</td>\n",
" </tr>\n",
......@@ -167,15 +167,15 @@
"</div>"
],
"text/plain": [
" compound concentration shadow_price lower_bound upper_bound\n",
"0 MNXM2 0.0 molar 0.5 100.0 micromolar 10.0 millimolar\n",
"1 MNXM5 0.0 molar 0.5 100.0 micromolar 10.0 millimolar\n",
"2 MNXM6 0.0 molar -0.5 100.0 micromolar 10.0 millimolar\n",
"3 MNXM13 0.0 molar 0.0 100.0 micromolar 10.0 millimolar\n",
"4 MNXM145 0.0 molar 0.0 100.0 micromolar 10.0 millimolar\n",
"5 MNXM160 0.0 molar 0.5 100.0 micromolar 10.0 millimolar\n",
"6 MNXM325 0.0 molar -0.5 100.0 micromolar 10.0 millimolar\n",
"7 MNXM429 0.0 molar 0.0 100.0 micromolar 10.0 millimolar"
" compound concentration shadow_price lower_bound upper_bound\n",
"0 MNXM2(l) 0.0 molar 5.000000e-01 100.0 micromolar 10.0 millimolar\n",
"1 MNXM5(aq) 0.0 molar 5.000000e-01 100.0 micromolar 10.0 millimolar\n",
"2 MNXM6(aq) 0.0 molar -5.000000e-01 100.0 micromolar 10.0 millimolar\n",
"3 MNXM13(aq) 0.0 molar 0.000000e+00 100.0 micromolar 10.0 millimolar\n",
"4 MNXM145(aq) 0.0 molar 1.110223e-16 100.0 micromolar 10.0 millimolar\n",
"5 MNXM160(aq) 0.0 molar 5.000000e-01 100.0 micromolar 10.0 millimolar\n",
"6 MNXM325(aq) 0.0 molar -5.000000e-01 100.0 micromolar 10.0 millimolar\n",
"7 MNXM429(aq) 0.0 molar 0.000000e+00 100.0 micromolar 10.0 millimolar"
]
},
"execution_count": 2,
......@@ -243,7 +243,7 @@
"outputs": [
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
......
......@@ -62,7 +62,6 @@ def main(args):
from equilibrator_api import (
Q_, ComponentContribution, Reaction, ureg) # isort:skip
ureg.default_format = ".2f"
p_h = Q_(args.ph)
assert p_h.check(None)
......@@ -80,7 +79,7 @@ def main(args):
assert temperature.check("[temperature]")
sys.stderr.write(f"pH = {p_h}\n")
sys.stderr.write(f"I = {ionic_strength:.2g}\n")
sys.stderr.write(f"I = {ionic_strength}\n")
sys.stderr.write(f"T = {temperature}\n")
sys.stderr.flush()
......
......@@ -32,58 +32,103 @@ import sys
from numpy import nan, sqrt
from equilibrator_api import ComponentContribution, Reaction
def main(args):
from equilibrator_api import (
Q_, ComponentContribution, Reaction) # isort:skip
if __name__ == '__main__':
p_h = Q_(args.ph)
assert p_h.check(None)
parser = argparse.ArgumentParser(
description='Calculate potentials for a number of reactions.')
parser.add_argument(
'outfile', type=argparse.FileType('w'),
help='path to output file')
parser.add_argument('--i', type=float,
help='ionic strength in M',
default=0.1)
parser.add_argument('--ph', type=float, help='pH level', default=7.0)
logging.getLogger().setLevel(logging.WARNING)
ionic_strength = Q_(args.i)
if ionic_strength.check(None):
ionic_strength *= Q_("M")
else:
assert ionic_strength.check("[concentration]")
args = parser.parse_args()
temperature = Q_(args.t)
if temperature.check(None):
temperature *= Q_("K")
else:
assert temperature.check("[temperature]")
sys.stderr.write(f"pH = {p_h}\n")
sys.stderr.write(f"I = {ionic_strength}\n")
sys.stderr.write(f"T = {temperature}\n")
sys.stderr.write('pH = %.1f\n' % args.ph)
sys.stderr.write('I = %.1f M\n' % args.i)
sys.stderr.write("Initializing the Component Contribution estimator.\n")
eq_api = ComponentContribution(p_h=p_h, ionic_strength=ionic_strength,
temperature=temperature)
ids = []
reactions = []
with open('data/iJO1366_reactions.csv', 'r') as f:
sys.stderr.write("Parsing model reactions.\n")
all_ids = []
comments = []
balanced_ids = [] # only those of real and balanced reactions
balanced_reactions = [] # a list of the reaction objects that are balanced
with open('iJO1366_reactions.csv', 'r') as f:
for row in csv.DictReader(f):
ids.append(row['bigg.reaction'])
all_ids.append(row['bigg.reaction'])
try:
reactions.append(Reaction.parse_formula(row['formula']))
rxn = Reaction.parse_formula(row['formula'])
sys.stderr.write(f"Reaction {row['bigg.reaction']}: {rxn}\n")
if rxn.is_empty():
comments.append("reaction is empty")
elif not rxn.is_balanced():
comments.append("reaction is not chemically balanced")
else:
balanced_ids.append(row['bigg.reaction'])
balanced_reactions.append(rxn)
comments.append("")
except ValueError as e:
print('warning: cannot parse reaction %s because of %s' %
(row['bigg.reaction'], str(e)))
reactions.append(Reaction({}))
continue
cc = ComponentContribution(pH=args.ph, ionic_strength=args.i)
sys.stderr.write("Calculating the ΔG'0 of all reactions in the model.\n")
dG0_prime, U = eq_api.standard_dg_prime_multi(balanced_reactions)
dG0_prime, U = cc.standard_dg_prime_multi(reactions)
rid_to_reaction = dict(zip(balanced_ids, balanced_reactions))
rid_to_dg0_prime = dict(zip(balanced_ids, dG0_prime))
rid_to_uncertainty = dict(zip(balanced_ids, U.diagonal().flat))
sys.stderr.write("Writing the results to .csv file.\n")
writer = csv.writer(args.outfile)
header = ['reaction', 'pH', 'ionic strength [M]', 'dG\'0 [kJ/mol]',
'uncertainty [kJ/mol]', 'ln(Reversibility Index)', 'comment']
header = ["reaction", "pH", "ionic strength [M]", "ΔG'0",
"uncertainty", "ln(Reversibility Index)", "comment"]
writer.writerow(header)
for s, r, dg0, u in zip(ids, reactions,
dG0_prime.flat, U.diagonal().flat):
row = [s, args.ph, args.i]
if r.is_empty():
row += [nan, nan, nan, 'reaction is empty']
elif r.check_full_reaction_balancing():
ln_RI = cc.reversibility_index(r)
row += ['%.2f' % dg0, '%.2f' % sqrt(u), '%.2f' % ln_RI, '']
else:
row += [nan, nan, nan, 'reaction is not chemically balanced']
writer.writerow(row)
for rid, comment in zip(all_ids, comments):
dg0 = nan
sigma = nan
ln_RI = nan
if rid in balanced_ids:
sigma = sqrt(rid_to_uncertainty[rid])
if sigma < Q_(100, "kJ/mol"):
dg0 = rid_to_dg0_prime[rid]
ln_RI = eq_api.ln_reversibility_index(rid_to_reaction[rid])
writer.writerow([rid, args.ph, args.i, dg0, sigma, ln_RI, comment])
args.outfile.flush()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Calculate potentials for a number of reactions.')
parser.add_argument(
'outfile', type=argparse.FileType('w'),
help='path to output file')
parser.add_argument('--ph', type=str,
help='pH level',
default="7.0")
parser.add_argument('--i', type=str,
help='ionic strength (in molar, default 0.25 M)',
default="0.25M")
parser.add_argument('--t', type=str,
help='temperature (in kalvin, default 298.15 K)',
default="298.15K")
logging.getLogger().setLevel(logging.WARNING)
args = parser.parse_args()
main(args)
bigg.reaction,formula
ex_14glucan_e,KEGG:C00718 <=>
ex_fe3hox_e,KEGG:C06227 <=>
ex_mobd_e,KEGG:C06232 <=>
14glucanabcpp,KEGG:C00001 + KEGG:C00002 + KEGG:C00718 <=> KEGG:C00008 + KEGG:C00009
14glucantexi, <=> KEGG:C00718
23pde2pp,KEGG:C00001 + KEGG:C02355 <=> KEGG:C01368
23pde4pp,KEGG:C00001 + KEGG:C02354 <=> KEGG:C05822
23pde7pp,KEGG:C00001 + KEGG:C02353 <=> KEGG:C01367
23pde9pp,KEGG:C00001 + KEGG:C06194 <=> KEGG:C06193
2mahmp,KEGG:C00001 + KEGG:C04752 <=> KEGG:C00009 + KEGG:C04556
3hcinnmh,KEGG:C00004 + KEGG:C00007 + KEGG:C12621 <=> KEGG:C00001 + KEGG:C00003 + KEGG:C12623
3hpppnh,KEGG:C00004 + KEGG:C00007 + KEGG:C11457 <=> KEGG:C00001 + KEGG:C00003 + KEGG:C04044
3kgk,KEGG:C00002 + KEGG:C00618 <=> KEGG:C00008 + KEGG:C14899
3ntd2pp,KEGG:C00001 + KEGG:C01368 <=> KEGG:C00009 + KEGG:C00299
3ntd4pp,KEGG:C00001 + KEGG:C05822 <=> KEGG:C00009 + KEGG:C00475
3ntd7pp,KEGG:C00001 + KEGG:C01367 <=> KEGG:C00009 + KEGG:C00212
3ntd9pp,KEGG:C00001 + KEGG:C06193 <=> KEGG:C00009 + KEGG:C00387
3oxcoat,KEGG:C00010 + KEGG:C02232 <=> KEGG:C00024 + KEGG:C00091
4hthrs,KEGG:C00001 + KEGG:C06055 <=> KEGG:C00009 + KEGG:C06056
5dglcnr,KEGG:C00005 + KEGG:C01062 <=> KEGG:C00006 + KEGG:C00257
abutd,KEGG:C00001 + KEGG:C00003 + KEGG:C00555 <=> KEGG:C00004 + KEGG:C00334
acacct,KEGG:C00024 + KEGG:C00164 <=> KEGG:C00033 + KEGG:C00332
acact1r,2 KEGG:C00024 <=> KEGG:C00010 + KEGG:C00332
acact2r,KEGG:C00024 + KEGG:C00136 <=> KEGG:C00010 + KEGG:C05269
acact3r,KEGG:C00024 + KEGG:C05270 <=> KEGG:C00010 + KEGG:C05267
acact4r,KEGG:C00024 + KEGG:C01944 <=> KEGG:C00010 + KEGG:C05265
acact5r,KEGG:C00024 + KEGG:C05274 <=> KEGG:C00010 + KEGG:C05263
acact6r,KEGG:C00024 + KEGG:C01832 <=> KEGG:C00010 + KEGG:C05261
acact7r,KEGG:C00024 + KEGG:C02593 <=> KEGG:C00010 + KEGG:C05259
acact8r,KEGG:C00010 + KEGG:C16216 <=> KEGG:C00024 + KEGG:C00154
acald,KEGG:C00003 + KEGG:C00010 + KEGG:C00084 <=> KEGG:C00004 + KEGG:C00024
acanthat,KEGG:C00024 + KEGG:C00108 <=> KEGG:C00010 + KEGG:C06332
acbipgt,KEGG:C00044 + KEGG:C06509 <=> KEGG:C00013 + KEGG:C06510
accoac,KEGG:C00002 + KEGG:C00024 + KEGG:C00288 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C00083
accoal,KEGG:C00002 + KEGG:C00010 + KEGG:C00163 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C00100
acgal1pppp,KEGG:C00001 + KEGG:C18060 <=> KEGG:C00009 + KEGG:C01132
acgam1pppp,KEGG:C00001 + KEGG:C04256 <=> KEGG:C00009 + KEGG:C00140
acgamk,KEGG:C00002 + KEGG:C00140 <=> KEGG:C00008 + KEGG:C00357
acgaptspp,KEGG:C00074 + KEGG:C00140 <=> KEGG:C00022 + KEGG:C00357
acgk,KEGG:C00002 + KEGG:C00624 <=> KEGG:C00008 + KEGG:C04133
achbs,KEGG:C00022 + KEGG:C00109 <=> KEGG:C00011 + KEGG:C00659
ackr,KEGG:C00002 + KEGG:C00033 <=> KEGG:C00008 + KEGG:C00227
acmanaptspp,KEGG:C00074 + KEGG:C00645 <=> KEGG:C00022 + KEGG:C04257
acmumptspp,KEGG:C00074 + KEGG:C02713 <=> KEGG:C00022 + KEGG:C16698
acnml,KEGG:C00270 <=> KEGG:C00022 + KEGG:C00645
acoad1f,KEGG:C00016 + KEGG:C00136 <=> KEGG:C00877 + KEGG:C01352
acoad2f,KEGG:C00016 + KEGG:C05270 <=> KEGG:C01352 + KEGG:C05271
acoad3f,KEGG:C00016 + KEGG:C01944 <=> KEGG:C01352 + KEGG:C05276
acoad4f,KEGG:C00016 + KEGG:C05274 <=> KEGG:C01352 + KEGG:C05275
acoad5f,KEGG:C00016 + KEGG:C01832 <=> KEGG:C01352 + KEGG:C03221
acoad6f,KEGG:C00016 + KEGG:C02593 <=> KEGG:C01352 + KEGG:C05273
acoad7f,KEGG:C00016 + KEGG:C00154 <=> KEGG:C01352 + KEGG:C05272
acoad8f,KEGG:C00016 + KEGG:C00412 <=> KEGG:C01352 + KEGG:C16218
acoda,KEGG:C00001 + KEGG:C00437 <=> KEGG:C00033 + KEGG:C00077
aconis,KEGG:C02341 <=> KEGG:C00417
aconmt,KEGG:C00019 + KEGG:C02341 <=> KEGG:C00021 + KEGG:C11514
aconta,KEGG:C00158 <=> KEGG:C00001 + KEGG:C00417
acontb,KEGG:C00001 + KEGG:C00417 <=> KEGG:C00311
acs,KEGG:C00002 + KEGG:C00010 + KEGG:C00033 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C00024
adcl,KEGG:C11355 <=> KEGG:C00022 + KEGG:C00568
adk1,KEGG:C00002 + KEGG:C00020 <=> 2 KEGG:C00008
adk3,KEGG:C00020 + KEGG:C00044 <=> KEGG:C00008 + KEGG:C00035
adk4,KEGG:C00020 + KEGG:C00081 <=> KEGG:C00008 + KEGG:C00104
admdc,KEGG:C00019 <=> KEGG:C00011 + KEGG:C01137
adncyc,KEGG:C00002 <=> KEGG:C00013 + KEGG:C00575
adnk1,KEGG:C00002 + KEGG:C00212 <=> KEGG:C00008 + KEGG:C00020
adocbik,KEGG:C00002 + KEGG:C06508 <=> KEGG:C00008 + KEGG:C06509
adocbls,KEGG:C05775 + KEGG:C06510 <=> KEGG:C00144 + KEGG:C00194
adprdp,KEGG:C00001 + KEGG:C00301 <=> KEGG:C00020 + KEGG:C03736
adpt,KEGG:C00119 + KEGG:C00147 <=> KEGG:C00013 + KEGG:C00020
adsk,KEGG:C00002 + KEGG:C00224 <=> KEGG:C00008 + KEGG:C00053
adsl1r,KEGG:C03794 <=> KEGG:C00020 + KEGG:C00122
adsl2r,KEGG:C04823 <=> KEGG:C00122 + KEGG:C04677
agdc,KEGG:C00001 + KEGG:C00357 <=> KEGG:C00033 + KEGG:C00352
agmhe,KEGG:C06397 <=> KEGG:C06398
agmt,KEGG:C00001 + KEGG:C00179 <=> KEGG:C00086 + KEGG:C00134
agpr,KEGG:C00006 + KEGG:C00009 + KEGG:C01250 <=> KEGG:C00005 + KEGG:C04133
aicart,KEGG:C00234 + KEGG:C04677 <=> KEGG:C00101 + KEGG:C04734
airc2,KEGG:C00002 + KEGG:C00288 + KEGG:C03373 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C15667
airc3,KEGG:C04751 <=> KEGG:C15667
akgdh,KEGG:C00003 + KEGG:C00010 + KEGG:C00026 <=> KEGG:C00004 + KEGG:C00011 + KEGG:C00091
alcd19,KEGG:C00004 + KEGG:C00577 <=> KEGG:C00003 + KEGG:C00116
alcd2x,KEGG:C00003 + KEGG:C00469 <=> KEGG:C00004 + KEGG:C00084
aldd19xr,KEGG:C00001 + KEGG:C00003 + KEGG:C00601 <=> KEGG:C00004 + KEGG:C00548
aldd2x,KEGG:C00001 + KEGG:C00003 + KEGG:C00084 <=> KEGG:C00004 + KEGG:C00033
aldd2y,KEGG:C00001 + KEGG:C00006 + KEGG:C00084 <=> KEGG:C00005 + KEGG:C00033
aldd3y,KEGG:C00001 + KEGG:C00006 + KEGG:C00479 <=> KEGG:C00005 + KEGG:C00163
aldd4,KEGG:C00001 + KEGG:C00003 + KEGG:C01412 <=> KEGG:C00004 + KEGG:C00246
alltn,KEGG:C00001 + KEGG:C01551 <=> KEGG:C00499
alr2,KEGG:C00005 + KEGG:C00546 <=> KEGG:C00006 + KEGG:C05235
altrh,KEGG:C00817 <=> KEGG:C00001 + KEGG:C00204
amanaper,KEGG:C04257 <=> KEGG:C00357
amank,KEGG:C00002 + KEGG:C00645 <=> KEGG:C00008 + KEGG:C04257
amaotr,KEGG:C00019 + KEGG:C01092 <=> KEGG:C01037 + KEGG:C04425
ampms2,KEGG:C00001 + KEGG:C00003 + KEGG:C03373 <=> KEGG:C00004 + 2 KEGG:C00058 + KEGG:C04556
ampn,KEGG:C00001 + KEGG:C00020 <=> KEGG:C00147 + KEGG:C03736
anprt,KEGG:C00108 + KEGG:C00119 <=> KEGG:C00013 + KEGG:C04302
aobutds,KEGG:C03214 <=> KEGG:C00011 + KEGG:C01888
ap4ah,KEGG:C00001 + KEGG:C01260 <=> 2 KEGG:C00008
ap4as,2 KEGG:C00002 <=> KEGG:C00013 + KEGG:C01260
ap5ah,KEGG:C00001 + KEGG:C04058 <=> KEGG:C00002 + KEGG:C00008
apcs,KEGG:C01137 + KEGG:C01672 <=> KEGG:C00170 + KEGG:C16565
appldhr,KEGG:C00004 + KEGG:C01888 <=> KEGG:C00003 + KEGG:C05771
apraur,KEGG:C00005 + KEGG:C01268 <=> KEGG:C00006 + KEGG:C04454
arbtptspp,KEGG:C00074 + KEGG:C06186 <=> KEGG:C00022 + KEGG:C06187
asad,KEGG:C00006 + KEGG:C00009 + KEGG:C00441 <=> KEGG:C00005 + KEGG:C03082
ascbpl,KEGG:C00001 + KEGG:C16186 <=> KEGG:C14899
atpm,KEGG:C00001 + KEGG:C00002 <=> KEGG:C00008 + KEGG:C00009
atpprt,KEGG:C00002 + KEGG:C00119 <=> KEGG:C00013 + KEGG:C02739
atps4rpp,KEGG:C00008 + KEGG:C00009 <=> KEGG:C00001 + KEGG:C00002
betaldhx,KEGG:C00001 + KEGG:C00003 + KEGG:C00576 <=> KEGG:C00004 + KEGG:C00719
betaldhy,KEGG:C00001 + KEGG:C00006 + KEGG:C00576 <=> KEGG:C00005 + KEGG:C00719
bpnt,KEGG:C00001 + KEGG:C00054 <=> KEGG:C00009 + KEGG:C00020
butct,KEGG:C00024 + KEGG:C00246 <=> KEGG:C00033 + KEGG:C00136
cat,2 KEGG:C00027 <=> 2 KEGG:C00001 + KEGG:C00007
cdgr,2 KEGG:C00005 + KEGG:C15996 <=> 2 KEGG:C00006 + KEGG:C16675
cdpmek,KEGG:C00002 + KEGG:C11435 <=> KEGG:C00008 + KEGG:C11436
chold,KEGG:C00003 + KEGG:C00114 <=> KEGG:C00004 + KEGG:C00576
chorm,KEGG:C00251 <=> KEGG:C00254
chors,KEGG:C01269 <=> KEGG:C00009 + KEGG:C00251
chrpl,KEGG:C00251 <=> KEGG:C00022 + KEGG:C00156
cinndo,KEGG:C00004 + KEGG:C00007 + KEGG:C00423 <=> KEGG:C00003 + KEGG:C12622
citl,KEGG:C00158 <=> KEGG:C00033 + KEGG:C00036
cmpn,KEGG:C00001 + KEGG:C00055 <=> KEGG:C00380 + KEGG:C03736
cpmps,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00013 + KEGG:C18239
cpppgo,KEGG:C00007 + KEGG:C03263 <=> 2 KEGG:C00001 + 2 KEGG:C00011 + KEGG:C01079
cs,KEGG:C00001 + KEGG:C00024 + KEGG:C00036 <=> KEGG:C00010 + KEGG:C00158
cyanst,KEGG:C00177 + KEGG:C00320 <=> KEGG:C00094 + KEGG:C01755
cyanstpp,KEGG:C00177 + KEGG:C00320 <=> KEGG:C00094 + KEGG:C01755
cytdk2,KEGG:C00044 + KEGG:C00475 <=> KEGG:C00035 + KEGG:C00055
cytk1,KEGG:C00002 + KEGG:C00055 <=> KEGG:C00008 + KEGG:C00112
cytk2,KEGG:C00002 + KEGG:C00239 <=> KEGG:C00008 + KEGG:C00705
dadk,KEGG:C00002 + KEGG:C00360 <=> KEGG:C00008 + KEGG:C00206
dbts,KEGG:C00002 + KEGG:C00011 + KEGG:C01037 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C01909
ddgalk,KEGG:C00002 + KEGG:C01216 <=> KEGG:C00008 + KEGG:C01286
ddglk,KEGG:C00002 + KEGG:C00204 <=> KEGG:C00008 + KEGG:C04442
ddpa,KEGG:C00001 + KEGG:C00074 + KEGG:C00279 <=> KEGG:C00009 + KEGG:C04691
ddpgala,KEGG:C01286 <=> KEGG:C00022 + KEGG:C00118
dgk1,KEGG:C00002 + KEGG:C00362 <=> KEGG:C00008 + KEGG:C00361
dhad1,KEGG:C04272 <=> KEGG:C00001 + KEGG:C00141
dhad2,KEGG:C04104 <=> KEGG:C00001 + KEGG:C00671
dhapt,KEGG:C00074 + KEGG:C00184 <=> KEGG:C00022 + KEGG:C00111
dhbd,KEGG:C00003 + KEGG:C04171 <=> KEGG:C00004 + KEGG:C00196
dhbs,KEGG:C00002 + KEGG:C00196 <=> KEGG:C00013 + KEGG:C04030
dhcind,KEGG:C00003 + KEGG:C12622 <=> KEGG:C00004 + KEGG:C12623
dhdpry,KEGG:C00005 + KEGG:C03340 <=> KEGG:C00006 + KEGG:C03972
dhdps,KEGG:C00022 + KEGG:C00441 <=> 2 KEGG:C00001 + KEGG:C03340
dhfr,KEGG:C00005 + KEGG:C00415 <=> KEGG:C00006 + KEGG:C00101
dhnpa2r,KEGG:C04874 <=> KEGG:C00266 + KEGG:C01300
dhppd,KEGG:C00003 + KEGG:C11588 <=> KEGG:C00004 + KEGG:C04044
dhps2,KEGG:C00568 + KEGG:C04807 <=> KEGG:C00013 + KEGG:C00921
dhptdnr,KEGG:C00005 + KEGG:C05649 <=> KEGG:C00006 + KEGG:C05650
dhptdnrn,KEGG:C00004 + KEGG:C05649 <=> KEGG:C00003 + KEGG:C05650
dhqs,KEGG:C04691 <=> KEGG:C00009 + KEGG:C00944
dhqti,KEGG:C00944 <=> KEGG:C00001 + KEGG:C02637
dkglcnr2x,KEGG:C00004 + KEGG:C02780 <=> KEGG:C00003 + KEGG:C01062
dkglcnr2y,KEGG:C00005 + KEGG:C02780 <=> KEGG:C00006 + KEGG:C01062
dmatt,KEGG:C00129 + KEGG:C00235 <=> KEGG:C00013 + KEGG:C00341
dmpps,KEGG:C00004 + KEGG:C11811 <=> KEGG:C00001 + KEGG:C00003 + KEGG:C00235
dnmppa,KEGG:C00001 + KEGG:C05925 <=> KEGG:C00009 + KEGG:C04874
dntppa,KEGG:C00001 + KEGG:C04895 <=> KEGG:C00013 + KEGG:C05925
dogulnr,KEGG:C00004 + KEGG:C04575 <=> KEGG:C00003 + KEGG:C00618
dpcoak,KEGG:C00002 + KEGG:C00882 <=> KEGG:C00008 + KEGG:C00010
drpa,KEGG:C00673 <=> KEGG:C00084 + KEGG:C00118
dtmpk,KEGG:C00002 + KEGG:C00364 <=> KEGG:C00008 + KEGG:C00363
duradx,KEGG:C00003 + KEGG:C00429 <=> KEGG:C00004 + KEGG:C00106
durik1,KEGG:C00002 + KEGG:C00526 <=> KEGG:C00008 + KEGG:C00365
duripp,KEGG:C00009 + KEGG:C00526 <=> KEGG:C00106 + KEGG:C00672
dutpdp,KEGG:C00001 + KEGG:C00460 <=> KEGG:C00013 + KEGG:C00365
dxprii,KEGG:C00005 + KEGG:C11437 <=> KEGG:C00006 + KEGG:C11434
dxps,KEGG:C00022 + KEGG:C00118 <=> KEGG:C00011 + KEGG:C11437
dxylk,KEGG:C00002 + KEGG:C06257 <=> KEGG:C00008 + KEGG:C11437
e4pd,KEGG:C00001 + KEGG:C00003 + KEGG:C00279 <=> KEGG:C00004 + KEGG:C03393
ecoah1,KEGG:C01144 <=> KEGG:C00001 + KEGG:C00877
ecoah2,KEGG:C05268 <=> KEGG:C00001 + KEGG:C05271
ecoah3,KEGG:C05266 <=> KEGG:C00001 + KEGG:C05276
ecoah4,KEGG:C05264 <=> KEGG:C00001 + KEGG:C05275
ecoah5,KEGG:C05262 <=> KEGG:C00001 + KEGG:C03221
ecoah6,KEGG:C05260 <=> KEGG:C00001 + KEGG:C05273
ecoah7,KEGG:C05258 <=> KEGG:C00001 + KEGG:C05272
ecoah8,KEGG:C16217 <=> KEGG:C00001 + KEGG:C16218
eda,KEGG:C04442 <=> KEGG:C00022 + KEGG:C00118
edd,KEGG:C00345 <=> KEGG:C00001 + KEGG:C04442
eno,KEGG:C00631 <=> KEGG:C00001 + KEGG:C00074
entcs,3 KEGG:C04030 + 3 KEGG:C05820 <=> 6 KEGG:C00020 + KEGG:C05821
f6pa,KEGG:C00085 <=> KEGG:C00118 + KEGG:C00184
f6pp,KEGG:C00001 + KEGG:C00085 <=> KEGG:C00009 + KEGG:C00095
facoae100,KEGG:C00001 + KEGG:C05274 <=> KEGG:C00010 + KEGG:C01571
facoae120,KEGG:C00001 + KEGG:C01832 <=> KEGG:C00010 + KEGG:C02679
facoae140,KEGG:C00001 + KEGG:C02593 <=> KEGG:C00010 + KEGG:C06424
facoae160,KEGG:C00001 + KEGG:C00154 <=> KEGG:C00010 + KEGG:C00249
facoae180,KEGG:C00001 + KEGG:C00412 <=> KEGG:C00010 + KEGG:C01530
facoae60,KEGG:C00001 + KEGG:C05270 <=> KEGG:C00010 + KEGG:C01585
facoae80,KEGG:C00001 + KEGG:C01944 <=> KEGG:C00010 + KEGG:C06423
facoal100t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C01571 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C05274
facoal120t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C02679 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C01832
facoal140t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C06424 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C02593
facoal160t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C00249 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C00154
facoal180t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C01530 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C00412
facoal60t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C01585 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C05270
facoal80t2pp,KEGG:C00002 + KEGG:C00010 + KEGG:C06423 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C01944
fadrx,KEGG:C00004 + KEGG:C00016 <=> KEGG:C00003 + KEGG:C01352
fadrx2,KEGG:C00005 + KEGG:C00016 <=> KEGG:C00006 + KEGG:C01352
fba,KEGG:C00354 <=> KEGG:C00111 + KEGG:C00118
fba3,KEGG:C00447 <=> KEGG:C00111 + KEGG:C00279
fbp,KEGG:C00001 + KEGG:C00354 <=> KEGG:C00009 + KEGG:C00085
fdmo,KEGG:C00007 + KEGG:C01847 + KEGG:C05123 <=> KEGG:C00001 + KEGG:C00061 + KEGG:C00094 + KEGG:C00266
fdmo2,KEGG:C00007 + KEGG:C01847 + KEGG:C11145 <=> KEGG:C00001 + KEGG:C00061 + KEGG:C00067 + KEGG:C00094
fdmo6,KEGG:C00007 + KEGG:C01847 + KEGG:C14179 <=> KEGG:C00001 + KEGG:C00048 + KEGG:C00061 + KEGG:C00094
fe3hoxabcpp,KEGG:C00001 + KEGG:C00002 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C06227
fe3hoxtonex,KEGG:C06227 <=>
fe3ri,KEGG:C01352 + 2 KEGG:C14819 <=> KEGG:C00016 + 2 KEGG:C14818
feropp,KEGG:C00007 + 4 KEGG:C14818 <=> 2 KEGG:C00001 + 4 KEGG:C14819
ffsd,KEGG:C00001 + KEGG:C02591 <=> KEGG:C00092 + KEGG:C00095
fhl,KEGG:C00058 <=> KEGG:C00011 + KEGG:C00282
flvr,KEGG:C00005 + KEGG:C00255 <=> KEGG:C00006 + KEGG:C01007
flvrx,KEGG:C00004 + KEGG:C00255 <=> KEGG:C00003 + KEGG:C01007
fmnat,KEGG:C00002 + KEGG:C00061 <=> KEGG:C00013 + KEGG:C00016
fmnrx,KEGG:C00004 + KEGG:C00061 <=> KEGG:C00003 + KEGG:C01847
fmnrx2,KEGG:C00005 + KEGG:C00061 <=> KEGG:C00006 + KEGG:C01847
fometri,KEGG:C03479 <=> KEGG:C00001 + KEGG:C00445
forct,KEGG:C00209 + KEGG:C00798 <=> KEGG:C00058 + KEGG:C00313
frupts2pp,KEGG:C00074 + KEGG:C00095 <=> KEGG:C00022 + KEGG:C00085
fthfd,KEGG:C00001 + KEGG:C00234 <=> KEGG:C00058 + KEGG:C00101
fthfli,KEGG:C00002 + KEGG:C00058 + KEGG:C00101 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C00234
g1pact,KEGG:C00024 + KEGG:C03783 <=> KEGG:C00010 + KEGG:C04256
g1ptt,KEGG:C00103 + KEGG:C00459 <=> KEGG:C00013 + KEGG:C00842
g1sat,KEGG:C03741 <=> KEGG:C00430
g2pp,KEGG:C00001 + KEGG:C02979 <=> KEGG:C00009 + KEGG:C00116
g2pppp,KEGG:C00001 + KEGG:C02979 <=> KEGG:C00009 + KEGG:C00116
g3pd2,KEGG:C00006 + KEGG:C00093 <=> KEGG:C00005 + KEGG:C00111
g3pt,KEGG:C00001 + KEGG:C00093 <=> KEGG:C00009 + KEGG:C00116
g5sads,KEGG:C01165 <=> KEGG:C00001 + KEGG:C03912
g5sd,KEGG:C00005 + KEGG:C03287 <=> KEGG:C00006 + KEGG:C00009 + KEGG:C01165
g6pdh2r,KEGG:C00006 + KEGG:C00092 <=> KEGG:C00005 + KEGG:C01236
gal1pppp,KEGG:C00001 + KEGG:C00446 <=> KEGG:C00009 + KEGG:C00124
galkr,KEGG:C00002 + KEGG:C00124 <=> KEGG:C00008 + KEGG:C00446
galm2pp,KEGG:C00962 <=> KEGG:C00124
galtptspp,KEGG:C00074 + KEGG:C01697 <=> KEGG:C00022 + KEGG:C06311
galui,KEGG:C00075 + KEGG:C00103 <=> KEGG:C00013 + KEGG:C00029
gamptspp,KEGG:C00074 + KEGG:C00329 <=> KEGG:C00022 + KEGG:C00352
gapd,KEGG:C00003 + KEGG:C00009 + KEGG:C00118 <=> KEGG:C00004 + KEGG:C00236
garft,KEGG:C00234 + KEGG:C03838 <=> KEGG:C00101 + KEGG:C04376
gart,KEGG:C00002 + KEGG:C00058 + KEGG:C03838 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C04376
gcaldd,KEGG:C00001 + KEGG:C00003 + KEGG:C00266 <=> KEGG:C00004 + KEGG:C00160
gdmane,KEGG:C01222 <=> KEGG:C14830
gdpdpk,KEGG:C00002 + KEGG:C00035 <=> KEGG:C00020 + KEGG:C01228
gdpmnh,KEGG:C00001 + KEGG:C00096 <=> KEGG:C00035 + KEGG:C00159
gdpmnp,KEGG:C00001 + KEGG:C00096 <=> KEGG:C00144 + KEGG:C00636
gdptpdp,KEGG:C00001 + KEGG:C04494 <=> KEGG:C00013 + KEGG:C00044
gggabadr,KEGG:C00001 + KEGG:C00006 + KEGG:C15700 <=> KEGG:C00005 + KEGG:C15767
ghbdhx,KEGG:C00004 + KEGG:C00232 <=> KEGG:C00003 + KEGG:C00989
gk1,KEGG:C00002 + KEGG:C00144 <=> KEGG:C00008 + KEGG:C00035
glcral,KEGG:C00679 <=> KEGG:C00022 + KEGG:C01146
glcrd,KEGG:C00818 <=> KEGG:C00001 + KEGG:C00679
glgc,KEGG:C00002 + KEGG:C00103 <=> KEGG:C00013 + KEGG:C00498
glxcl,2 KEGG:C00048 <=> KEGG:C00011 + KEGG:C01146
glyat,KEGG:C00024 + KEGG:C00037 <=> KEGG:C00010 + KEGG:C03214
glycdx,KEGG:C00003 + KEGG:C00116 <=> KEGG:C00004 + KEGG:C00184
glycltdx,KEGG:C00004 + KEGG:C00048 <=> KEGG:C00003 + KEGG:C00160
glycltdy,KEGG:C00005 + KEGG:C00048 <=> KEGG:C00006 + KEGG:C00160
glyk,KEGG:C00002 + KEGG:C00116 <=> KEGG:C00008 + KEGG:C00093
gmand,KEGG:C00096 <=> KEGG:C00001 + KEGG:C01222
gmhepat,KEGG:C00002 + KEGG:C07838 <=> KEGG:C00013 + KEGG:C06397
gmhepk,KEGG:C00002 + KEGG:C07836 <=> KEGG:C00008 + KEGG:C11472
gmheppa,KEGG:C00001 + KEGG:C11472 <=> KEGG:C00009 + KEGG:C07838
gnk,KEGG:C00002 + KEGG:C00257 <=> KEGG:C00008 + KEGG:C00345
gofucr,KEGG:C00005 + KEGG:C14830 <=> KEGG:C00006 + KEGG:C00325
gp4gh,KEGG:C00001 + KEGG:C01261 <=> 2 KEGG:C00035
gpdda1,KEGG:C00001 + KEGG:C00670 <=> KEGG:C00093 + KEGG:C00114
gpdda1pp,KEGG:C00001 + KEGG:C00670 <=> KEGG:C00093 + KEGG:C00114
gpdda2,KEGG:C00001 + KEGG:C01233 <=> KEGG:C00093 + KEGG:C00189
gpdda2pp,KEGG:C00001 + KEGG:C01233 <=> KEGG:C00093 + KEGG:C00189
gpdda4,KEGG:C00001 + KEGG:C03274 <=> KEGG:C00093 + KEGG:C00116
gpdda4pp,KEGG:C00001 + KEGG:C03274 <=> KEGG:C00093 + KEGG:C00116
gpdda5,KEGG:C00001 + KEGG:C01225 <=> KEGG:C00093 + KEGG:C00137
gpdda5pp,KEGG:C00001 + KEGG:C01225 <=> KEGG:C00093 + KEGG:C00137
grtt,KEGG:C00129 + KEGG:C00341 <=> KEGG:C00013 + KEGG:C00448
gsnk,KEGG:C00002 + KEGG:C00387 <=> KEGG:C00008 + KEGG:C00144
gtpci,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00058 + KEGG:C04895
gtpcii2,3 KEGG:C00001 + KEGG:C00044 <=> KEGG:C00013 + KEGG:C00058 + KEGG:C01304
gtpdpdp,KEGG:C00001 + KEGG:C04494 <=> KEGG:C00009 + KEGG:C01228
gtpdpk,KEGG:C00002 + KEGG:C00044 <=> KEGG:C00020 + KEGG:C04494
guacyc,KEGG:C00044 <=> KEGG:C00013 + KEGG:C00942
guaprt,KEGG:C00119 + KEGG:C00242 <=> KEGG:C00013 + KEGG:C00144
gui1,KEGG:C00191 <=> KEGG:C00905
gur1pppp,KEGG:C00001 + KEGG:C05385 <=> KEGG:C00009 + KEGG:C00191
h2so,2 KEGG:C00007 + KEGG:C00283 <=> KEGG:C00059
hacd1,KEGG:C00004 + KEGG:C00332 <=> KEGG:C00003 + KEGG:C01144
hacd2,KEGG:C00004 + KEGG:C05269 <=> KEGG:C00003 + KEGG:C05268
hacd3,KEGG:C00004 + KEGG:C05267 <=> KEGG:C00003 + KEGG:C05266
hacd4,KEGG:C00004 + KEGG:C05265 <=> KEGG:C00003 + KEGG:C05264
hacd5,KEGG:C00004 + KEGG:C05263 <=> KEGG:C00003 + KEGG:C05262
hacd6,KEGG:C00004 + KEGG:C05261 <=> KEGG:C00003 + KEGG:C05260
hacd7,KEGG:C00004 + KEGG:C05259 <=> KEGG:C00003 + KEGG:C05258
hacd8,KEGG:C00004 + KEGG:C16216 <=> KEGG:C00003 + KEGG:C16217
hadpcoadh3,KEGG:C00003 + KEGG:C14145 <=> KEGG:C00004 + KEGG:C02232
hbzopt,KEGG:C00156 + KEGG:C04146 <=> KEGG:C00013 + KEGG:C05809
hco3e,KEGG:C00001 + KEGG:C00011 <=> KEGG:C00288
hetzk,KEGG:C00002 + KEGG:C04294 <=> KEGG:C00008 + KEGG:C04327
hex4,KEGG:C00002 + KEGG:C00159 <=> KEGG:C00008 + KEGG:C00275
hex7,KEGG:C00002 + KEGG:C00095 <=> KEGG:C00008 + KEGG:C00085
histp,KEGG:C00001 + KEGG:C01100 <=> KEGG:C00009 + KEGG:C00860
hknddh,KEGG:C00001 + KEGG:C04479 <=> KEGG:C00042 + KEGG:C00596
hmpk1,KEGG:C00002 + KEGG:C01279 <=> KEGG:C00008 + KEGG:C04556
hopntal,KEGG:C03589 <=> KEGG:C00022 + KEGG:C00084
hppk2,KEGG:C00002 + KEGG:C01300 <=> KEGG:C00020 + KEGG:C04807
hpppndo,KEGG:C00007 + KEGG:C04044 <=> KEGG:C04479
hpyri,KEGG:C00168 <=> KEGG:C01146
hxand,KEGG:C00001 + KEGG:C00003 + KEGG:C00262 <=> KEGG:C00004 + KEGG:C00385
hxct,KEGG:C00024 + KEGG:C01585 <=> KEGG:C00033 + KEGG:C05270
hxprt,KEGG:C00119 + KEGG:C00262 <=> KEGG:C00013 + KEGG:C00130
hypoe,KEGG:C00001 + KEGG:C00647 <=> KEGG:C00009 + KEGG:C00534
icdhyr,KEGG:C00006 + KEGG:C00311 <=> KEGG:C00005 + KEGG:C00011 + KEGG:C00026
ichors,KEGG:C00251 <=> KEGG:C00885
ichorsi,KEGG:C00251 <=> KEGG:C00885
ichort,KEGG:C00001 + KEGG:C00885 <=> KEGG:C00022 + KEGG:C04171
icl,KEGG:C00311 <=> KEGG:C00042 + KEGG:C00048
igpdh,KEGG:C04666 <=> KEGG:C00001 + KEGG:C01267
igps,KEGG:C01302 <=> KEGG:C00001 + KEGG:C00011 + KEGG:C03506
impc,KEGG:C00001 + KEGG:C00130 <=> KEGG:C04734
impd,KEGG:C00001 + KEGG:C00003 + KEGG:C00130 <=> KEGG:C00004 + KEGG:C00655
insk,KEGG:C00002 + KEGG:C00294 <=> KEGG:C00008 + KEGG:C00130
ipddi,KEGG:C00129 <=> KEGG:C00235
ipdps,KEGG:C00004 + KEGG:C11811 <=> KEGG:C00001 + KEGG:C00003 + KEGG:C00129
ipmd,KEGG:C00003 + KEGG:C04411 <=> KEGG:C00004 + KEGG:C04236
ippmia,KEGG:C04411 <=> KEGG:C00001 + KEGG:C02631
ippmib,KEGG:C00001 + KEGG:C02631 <=> KEGG:C02504
ipps,KEGG:C00001 + KEGG:C00024 + KEGG:C00141 <=> KEGG:C00010 + KEGG:C02504
kara2,KEGG:C00005 + KEGG:C00659 <=> KEGG:C00006 + KEGG:C04104
kdoct2,KEGG:C00063 + KEGG:C01187 <=> KEGG:C00013 + KEGG:C04121
kdopp,KEGG:C00001 + KEGG:C04478 <=> KEGG:C00009 + KEGG:C01187
kdops,KEGG:C00001 + KEGG:C00074 + KEGG:C01112 <=> KEGG:C00009 + KEGG:C04478
lipatpt,KEGG:C00002 + KEGG:C00725 <=> KEGG:C00013 + KEGG:C16238
m1pd,KEGG:C00003 + KEGG:C00644 <=> KEGG:C00004 + KEGG:C00085
maltatr,KEGG:C00024 + KEGG:C00208 <=> KEGG:C00010 + KEGG:C02130
maltptspp,KEGG:C00074 + KEGG:C00208 <=> KEGG:C00022 + KEGG:C02995
man1pt2,KEGG:C00035 + KEGG:C00636 <=> KEGG:C00009 + KEGG:C00096
man6pi,KEGG:C00275 <=> KEGG:C00085
manao,KEGG:C00003 + KEGG:C00514 <=> KEGG:C00004 + KEGG:C00905
manglycptspp,KEGG:C00074 + KEGG:C11544 <=> KEGG:C00022 + KEGG:C16699
manptspp,KEGG:C00074 + KEGG:C00159 <=> KEGG:C00022 + KEGG:C00275
mcitd,KEGG:C02225 <=> KEGG:C00001 + KEGG:C04225
mcitl2,KEGG:C04593 <=> KEGG:C00022 + KEGG:C00042
mcits,KEGG:C00001 + KEGG:C00036 + KEGG:C00100 <=> KEGG:C00010 + KEGG:C02225
mcpst,KEGG:C00177 + KEGG:C00957 <=> KEGG:C00022 + KEGG:C01755
mecdps,KEGG:C11436 <=> KEGG:C00055 + KEGG:C11453
mepct,KEGG:C00063 + KEGG:C11434 <=> KEGG:C00013 + KEGG:C11435
mgsa,KEGG:C00111 <=> KEGG:C00009 + KEGG:C00546
micitdr,KEGG:C00001 + KEGG:C04225 <=> KEGG:C04593
mltp1,KEGG:C00009 + KEGG:C06218 <=> KEGG:C00103 + KEGG:C02013
mltp2,KEGG:C00009 + KEGG:C01936 <=> KEGG:C00103 + KEGG:C06218
mltp3,KEGG:C00009 + KEGG:C06216 <=> KEGG:C00103 + KEGG:C01936
mn6pp,KEGG:C00001 + KEGG:C00275 <=> KEGG:C00009 + KEGG:C00159
mnlptspp,KEGG:C00074 + KEGG:C00392 <=> KEGG:C00022 + KEGG:C00644
mnnh,KEGG:C00514 <=> KEGG:C00001 + KEGG:C00204
moat,KEGG:C04121 + KEGG:C04919 <=> KEGG:C00055 + KEGG:C06024
moat2,KEGG:C04121 + KEGG:C06024 <=> KEGG:C00055 + KEGG:C06025
mobdabcpp,KEGG:C00001 + KEGG:C00002 + KEGG:C06232 <=> KEGG:C00008 + KEGG:C00009
mobdtex, <=> KEGG:C06232
mohmt,KEGG:C00001 + KEGG:C00141 + KEGG:C00143 <=> KEGG:C00101 + KEGG:C00966
msar,KEGG:C00005 + KEGG:C00222 <=> KEGG:C00006 + KEGG:C01013
mtan,KEGG:C00001 + KEGG:C00170 <=> KEGG:C00147 + KEGG:C03089
mthfc,KEGG:C00001 + KEGG:C00445 <=> KEGG:C00234
mthfd,KEGG:C00006 + KEGG:C00143 <=> KEGG:C00005 + KEGG:C00445
mthfr2,KEGG:C00004 + KEGG:C00143 <=> KEGG:C00003 + KEGG:C00440
nacoda,KEGG:C00001 + KEGG:C01250 <=> KEGG:C00033 + KEGG:C01165
naddp,KEGG:C00001 + KEGG:C00003 <=> KEGG:C00020 + KEGG:C00455
nadk,KEGG:C00002 + KEGG:C00003 <=> KEGG:C00006 + KEGG:C00008
nadn,KEGG:C00001 + KEGG:C00003 <=> KEGG:C00153 + KEGG:C00301
nadppps,KEGG:C00001 + KEGG:C00006 <=> KEGG:C00003 + KEGG:C00009
nadtrhd,KEGG:C00003 + KEGG:C00005 <=> KEGG:C00004 + KEGG:C00006
namnpp,KEGG:C00001 + KEGG:C00002 + KEGG:C00119 + KEGG:C00253 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C00013 + KEGG:C01185
ndpk1,KEGG:C00002 + KEGG:C00035 <=> KEGG:C00008 + KEGG:C00044
ndpk2,KEGG:C00002 + KEGG:C00015 <=> KEGG:C00008 + KEGG:C00075
ndpk3,KEGG:C00002 + KEGG:C00112 <=> KEGG:C00008 + KEGG:C00063
ndpk4,KEGG:C00002 + KEGG:C00363 <=> KEGG:C00008 + KEGG:C00459
ndpk5,KEGG:C00002 + KEGG:C00361 <=> KEGG:C00008 + KEGG:C00286
ndpk6,KEGG:C00002 + KEGG:C01346 <=> KEGG:C00008 + KEGG:C00460
ndpk7,KEGG:C00002 + KEGG:C00705 <=> KEGG:C00008 + KEGG:C00458
ndpk8,KEGG:C00002 + KEGG:C00206 <=> KEGG:C00008 + KEGG:C00131
nhfrbo,KEGG:C00004 + 2 KEGG:C00533 <=> KEGG:C00001 + KEGG:C00003 + KEGG:C00887
nmnat,KEGG:C00002 + KEGG:C00455 <=> KEGG:C00003 + KEGG:C00013
nmnn,KEGG:C00001 + KEGG:C00455 <=> KEGG:C00153 + KEGG:C03736
nmnt7pp,KEGG:C00001 + KEGG:C00455 <=> KEGG:C00153 + KEGG:C03736
nnatr,KEGG:C00002 + KEGG:C01185 <=> KEGG:C00013 + KEGG:C00857
nndmbrt,KEGG:C01185 + KEGG:C03114 <=> KEGG:C00253 + KEGG:C04778
nndpr,KEGG:C00119 + KEGG:C03722 <=> KEGG:C00011 + KEGG:C00013 + KEGG:C01185
nodox,KEGG:C00004 + 2 KEGG:C00007 + 2 KEGG:C00533 <=> KEGG:C00003 + 2 KEGG:C00244
nodoy,KEGG:C00005 + 2 KEGG:C00007 + 2 KEGG:C00533 <=> KEGG:C00006 + 2 KEGG:C00244
ntd1,KEGG:C00001 + KEGG:C00365 <=> KEGG:C00009 + KEGG:C00526
ntd10,KEGG:C00001 + KEGG:C00655 <=> KEGG:C00009 + KEGG:C01762
ntd10pp,KEGG:C00001 + KEGG:C00655 <=> KEGG:C00009 + KEGG:C01762
ntd11,KEGG:C00001 + KEGG:C00130 <=> KEGG:C00009 + KEGG:C00294
ntd11pp,KEGG:C00001 + KEGG:C00130 <=> KEGG:C00009 + KEGG:C00294
ntd12,KEGG:C00001 + KEGG:C06196 <=> KEGG:C00009 + KEGG:C05512
ntd12pp,KEGG:C00001 + KEGG:C06196 <=> KEGG:C00009 + KEGG:C05512
ntd1pp,KEGG:C00001 + KEGG:C00365 <=> KEGG:C00009 + KEGG:C00526
ntd2,KEGG:C00001 + KEGG:C00105 <=> KEGG:C00009 + KEGG:C00299
ntd2pp,KEGG:C00001 + KEGG:C00105 <=> KEGG:C00009 + KEGG:C00299
ntd3,KEGG:C00001 + KEGG:C00239 <=> KEGG:C00009 + KEGG:C00881
ntd3pp,KEGG:C00001 + KEGG:C00239 <=> KEGG:C00009 + KEGG:C00881
ntd4,KEGG:C00001 + KEGG:C00055 <=> KEGG:C00009 + KEGG:C00475
ntd4pp,KEGG:C00001 + KEGG:C00055 <=> KEGG:C00009 + KEGG:C00475
ntd5,KEGG:C00001 + KEGG:C00364 <=> KEGG:C00009 + KEGG:C00214
ntd5pp,KEGG:C00001 + KEGG:C00364 <=> KEGG:C00009 + KEGG:C00214
ntd6,KEGG:C00001 + KEGG:C00360 <=> KEGG:C00009 + KEGG:C00559
ntd6pp,KEGG:C00001 + KEGG:C00360 <=> KEGG:C00009 + KEGG:C00559
ntd7,KEGG:C00001 + KEGG:C00020 <=> KEGG:C00009 + KEGG:C00212
ntd7pp,KEGG:C00001 + KEGG:C00020 <=> KEGG:C00009 + KEGG:C00212
ntd8,KEGG:C00001 + KEGG:C00362 <=> KEGG:C00009 + KEGG:C00330
ntd8pp,KEGG:C00001 + KEGG:C00362 <=> KEGG:C00009 + KEGG:C00330
ntd9,KEGG:C00001 + KEGG:C00144 <=> KEGG:C00009 + KEGG:C00387
ntd9pp,KEGG:C00001 + KEGG:C00144 <=> KEGG:C00009 + KEGG:C00387
ntp1,KEGG:C00001 + KEGG:C00002 <=> KEGG:C00008 + KEGG:C00009
ntp10,KEGG:C00001 + KEGG:C00081 <=> KEGG:C00009 + KEGG:C00104
ntp11,KEGG:C00001 + KEGG:C01345 <=> KEGG:C00009 + KEGG:C01344
ntp3,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00009 + KEGG:C00035
ntp3pp,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00009 + KEGG:C00035
ntp5,KEGG:C00001 + KEGG:C00063 <=> KEGG:C00009 + KEGG:C00112
ntpp1,KEGG:C00001 + KEGG:C00286 <=> KEGG:C00013 + KEGG:C00362
ntpp10,KEGG:C00001 + KEGG:C01345 <=> KEGG:C00013 + KEGG:C06196
ntpp11,KEGG:C00001 + KEGG:C00700 <=> KEGG:C00013 + KEGG:C00655
ntpp2,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00013 + KEGG:C00144
ntpp3,KEGG:C00001 + KEGG:C00458 <=> KEGG:C00013 + KEGG:C00239
ntpp4,KEGG:C00001 + KEGG:C00063 <=> KEGG:C00013 + KEGG:C00055
ntpp5,KEGG:C00001 + KEGG:C00131 <=> KEGG:C00013 + KEGG:C00360
ntpp6,KEGG:C00001 + KEGG:C00002 <=> KEGG:C00013 + KEGG:C00020
ntpp7,KEGG:C00001 + KEGG:C00459 <=> KEGG:C00013 + KEGG:C00364
ntpp8,KEGG:C00001 + KEGG:C00075 <=> KEGG:C00013 + KEGG:C00105
ntpp9,KEGG:C00001 + KEGG:C00081 <=> KEGG:C00013 + KEGG:C00130
ntptp1,KEGG:C00001 + KEGG:C00286 <=> KEGG:C00330 + KEGG:C00536
ntptp2,KEGG:C00001 + KEGG:C00044 <=> KEGG:C00387 + KEGG:C00536
oaadc,KEGG:C00036 <=> KEGG:C00011 + KEGG:C00022
obtfl,KEGG:C00010 + KEGG:C00109 <=> KEGG:C00058 + KEGG:C00100
octdps,5 KEGG:C00129 + KEGG:C00448 <=> 5 KEGG:C00013 + KEGG:C04146
ohphm,KEGG:C00019 + KEGG:C05811 <=> KEGG:C00021 + KEGG:C05812
omcdc,KEGG:C04236 <=> KEGG:C00011 + KEGG:C00233
ompdc,KEGG:C01103 <=> KEGG:C00011 + KEGG:C00105
op4enh,KEGG:C00001 + KEGG:C00596 <=> KEGG:C03589
ophbdc,KEGG:C05809 <=> KEGG:C00011 + KEGG:C05810
ophhx,0.5 KEGG:C00007 + KEGG:C05810 <=> KEGG:C05811
ophhx3,3 KEGG:C00001 + 2 KEGG:C00002 + KEGG:C00003 + KEGG:C05810 <=> KEGG:C00004 + 2 KEGG:C00008 + 2 KEGG:C00009 + KEGG:C05811
orndc,KEGG:C00077 <=> KEGG:C00011 + KEGG:C00134
orpt,KEGG:C00013 + KEGG:C01103 <=> KEGG:C00119 + KEGG:C00295
oxamtc,KEGG:C00009 + KEGG:C00802 <=> KEGG:C00169 + KEGG:C01444
oxcdc,KEGG:C00313 <=> KEGG:C00011 + KEGG:C00798
paccoal,KEGG:C00002 + KEGG:C00010 + KEGG:C00548 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C00582
papsr,KEGG:C00053 + KEGG:C00342 <=> KEGG:C00054 + KEGG:C00094 + KEGG:C00343
pde1,KEGG:C00001 + KEGG:C00575 <=> KEGG:C00020
pde4,KEGG:C00001 + KEGG:C00942 <=> KEGG:C00144
pdh,KEGG:C00003 + KEGG:C00010 + KEGG:C00022 <=> KEGG:C00004 + KEGG:C00011 + KEGG:C00024
pdx5po2,KEGG:C00003 + KEGG:C00627 <=> KEGG:C00004 + KEGG:C00018
pdx5poi,KEGG:C00007 + KEGG:C00627 <=> KEGG:C00018 + KEGG:C00027
pdx5ps,KEGG:C00003 + KEGG:C06055 + KEGG:C11437 <=> 2 KEGG:C00001 + KEGG:C00004 + KEGG:C00009 + KEGG:C00011 + KEGG:C00627
pdxpp,KEGG:C00001 + KEGG:C00627 <=> KEGG:C00009 + KEGG:C00314
perd,KEGG:C00003 + KEGG:C03393 <=> KEGG:C00004 + KEGG:C06054
pfk,KEGG:C00002 + KEGG:C00085 <=> KEGG:C00008 + KEGG:C00354
pfk_3,KEGG:C00002 + KEGG:C05382 <=> KEGG:C00008 + KEGG:C00447
pfl,KEGG:C00010 + KEGG:C00022 <=> KEGG:C00024 + KEGG:C00058
pgamt,KEGG:C03783 <=> KEGG:C00352
pgcd,KEGG:C00003 + KEGG:C00197 <=> KEGG:C00004 + KEGG:C03232
pgi,KEGG:C00092 <=> KEGG:C00085
pgk,KEGG:C00002 + KEGG:C00197 <=> KEGG:C00008 + KEGG:C00236
pgl,KEGG:C00001 + KEGG:C01236 <=> KEGG:C00345
pglycp,KEGG:C00001 + KEGG:C00988 <=> KEGG:C00009 + KEGG:C00160
pgm,KEGG:C00631 <=> KEGG:C00197
pgmt,KEGG:C00103 <=> KEGG:C00092
phytspp,6 KEGG:C00001 + KEGG:C01204 <=> 6 KEGG:C00009 + KEGG:C00137
pmanm,KEGG:C00636 <=> KEGG:C00275
pmdpht,KEGG:C00001 + KEGG:C04454 <=> KEGG:C00009 + KEGG:C04732
pmpk,KEGG:C00002 + KEGG:C04556 <=> KEGG:C00008 + KEGG:C04752
ppa,KEGG:C00001 + KEGG:C00013 <=> 2 KEGG:C00009
ppa2,KEGG:C00001 + KEGG:C00536 <=> KEGG:C00009 + KEGG:C00013
ppakr,KEGG:C00008 + KEGG:C02876 <=> KEGG:C00002 + KEGG:C00163
ppbngs,2 KEGG:C00430 <=> 2 KEGG:C00001 + KEGG:C00931
ppc,KEGG:C00001 + KEGG:C00011 + KEGG:C00074 <=> KEGG:C00009 + KEGG:C00036
ppcdc,KEGG:C04352 <=> KEGG:C00011 + KEGG:C01134
ppck,KEGG:C00002 + KEGG:C00036 <=> KEGG:C00008 + KEGG:C00011 + KEGG:C00074
ppcsct,KEGG:C00042 + KEGG:C00100 <=> KEGG:C00091 + KEGG:C00163
ppgppdp,KEGG:C00001 + KEGG:C01228 <=> KEGG:C00013 + KEGG:C00035
ppk2r,KEGG:C00002 + KEGG:C00013 <=> KEGG:C00008 + KEGG:C00536
ppkr,KEGG:C00002 + KEGG:C00009 <=> KEGG:C00008 + KEGG:C00013
ppm,KEGG:C00620 <=> KEGG:C03736
ppm2,KEGG:C00672 <=> KEGG:C00673
ppndh,KEGG:C00254 <=> KEGG:C00001 + KEGG:C00011 + KEGG:C00166
pppgo,1.5 KEGG:C00007 + KEGG:C01079 <=> 3 KEGG:C00001 + KEGG:C02191
pppgo3,3 KEGG:C00122 + KEGG:C01079 <=> 3 KEGG:C00042 + KEGG:C02191
pppndo,KEGG:C00004 + KEGG:C00007 + KEGG:C05629 <=> KEGG:C00003 + KEGG:C11588
pps,KEGG:C00001 + KEGG:C00002 + KEGG:C00022 <=> KEGG:C00009 + KEGG:C00020 + KEGG:C00074
ppthpp,KEGG:C00001 + KEGG:C06701 <=> KEGG:C00009 + KEGG:C00282
pragsr,KEGG:C00002 + KEGG:C00037 + KEGG:C03090 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C03838
prais,KEGG:C00002 + KEGG:C04640 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C03373
praii,KEGG:C04302 <=> KEGG:C01302
prampc,KEGG:C00001 + KEGG:C02741 <=> KEGG:C04896
pratpp,KEGG:C00001 + KEGG:C02739 <=> KEGG:C00013 + KEGG:C02741
prmici,KEGG:C04896 <=> KEGG:C04916
prpps,KEGG:C00002 + KEGG:C03736 <=> KEGG:C00020 + KEGG:C00119
pscvt,KEGG:C00074 + KEGG:C03175 <=> KEGG:C00009 + KEGG:C01269
pta2,KEGG:C00009 + KEGG:C00100 <=> KEGG:C00010 + KEGG:C02876
ptar,KEGG:C00009 + KEGG:C00024 <=> KEGG:C00010 + KEGG:C00227
ptpati,KEGG:C00002 + KEGG:C01134 <=> KEGG:C00013 + KEGG:C00882
punp1,KEGG:C00009 + KEGG:C00212 <=> KEGG:C00147 + KEGG:C00620
punp2,KEGG:C00009 + KEGG:C00559 <=> KEGG:C00147 + KEGG:C00672
punp3,KEGG:C00009 + KEGG:C00387 <=> KEGG:C00242 + KEGG:C00620
punp4,KEGG:C00009 + KEGG:C00330 <=> KEGG:C00242 + KEGG:C00672
punp5,KEGG:C00009 + KEGG:C00294 <=> KEGG:C00262 + KEGG:C00620
punp6,KEGG:C00009 + KEGG:C05512 <=> KEGG:C00262 + KEGG:C00672
punp7,KEGG:C00009 + KEGG:C01762 <=> KEGG:C00385 + KEGG:C00620
pydamk,KEGG:C00002 + KEGG:C00534 <=> KEGG:C00008 + KEGG:C00647
pydxk,KEGG:C00002 + KEGG:C00250 <=> KEGG:C00008 + KEGG:C00018
pydxnk,KEGG:C00002 + KEGG:C00314 <=> KEGG:C00008 + KEGG:C00627
pydxpp,KEGG:C00001 + KEGG:C00018 <=> KEGG:C00009 + KEGG:C00250
pyk,KEGG:C00008 + KEGG:C00074 <=> KEGG:C00002 + KEGG:C00022
pynp2r,KEGG:C00009 + KEGG:C00299 <=> KEGG:C00106 + KEGG:C00620
quindh,KEGG:C00003 + KEGG:C00296 <=> KEGG:C00004 + KEGG:C00944
qulns,KEGG:C00111 + KEGG:C05840 <=> 2 KEGG:C00001 + KEGG:C00009 + KEGG:C03722
r15bpk,KEGG:C00002 + KEGG:C01151 <=> KEGG:C00008 + KEGG:C00119
r1pk,KEGG:C00002 + KEGG:C00620 <=> KEGG:C00008 + KEGG:C01151
rbfk,KEGG:C00002 + KEGG:C00255 <=> KEGG:C00008 + KEGG:C00061
rbfsa,KEGG:C04732 + KEGG:C15556 <=> 2 KEGG:C00001 + KEGG:C00009 + KEGG:C04332
rbfsb,2 KEGG:C04332 <=> KEGG:C00255 + KEGG:C04732
rmi,KEGG:C00507 <=> KEGG:C00861
rmk,KEGG:C00002 + KEGG:C00861 <=> KEGG:C00008 + KEGG:C01131
rndr1,KEGG:C00008 + KEGG:C00342 <=> KEGG:C00001 + KEGG:C00206 + KEGG:C00343
rndr2,KEGG:C00035 + KEGG:C00342 <=> KEGG:C00001 + KEGG:C00343 + KEGG:C00361
rndr3,KEGG:C00112 + KEGG:C00342 <=> KEGG:C00001 + KEGG:C00343 + KEGG:C00705
rndr4,KEGG:C00015 + KEGG:C00342 <=> KEGG:C00001 + KEGG:C00343 + KEGG:C01346
rz5pp,KEGG:C00001 + KEGG:C04778 <=> KEGG:C00009 + KEGG:C05775
s7pi,KEGG:C05382 <=> KEGG:C07836
sadt2,KEGG:C00001 + KEGG:C00002 + KEGG:C00044 + KEGG:C00059 <=> KEGG:C00009 + KEGG:C00013 + KEGG:C00035 + KEGG:C00224
sarcox,KEGG:C00001 + KEGG:C00007 + KEGG:C00213 <=> KEGG:C00027 + KEGG:C00037 + KEGG:C00067
sbtpd,KEGG:C00003 + KEGG:C01096 <=> KEGG:C00004 + KEGG:C00085
sdpds,KEGG:C00001 + KEGG:C04421 <=> KEGG:C00042 + KEGG:C00666
selnps,KEGG:C00001 + KEGG:C00002 + KEGG:C01528 <=> KEGG:C00009 + KEGG:C00020 + KEGG:C05172
sephchcs,KEGG:C00026 + KEGG:C00885 <=> KEGG:C00011 + KEGG:C16519
sgsad,KEGG:C00001 + KEGG:C00003 + KEGG:C05932 <=> KEGG:C00004 + KEGG:C05931
shchcs3,KEGG:C16519 <=> KEGG:C00022 + KEGG:C05817
shchd2,KEGG:C00003 + KEGG:C02463 <=> KEGG:C00004 + KEGG:C05778
shk3dr,KEGG:C00005 + KEGG:C02637 <=> KEGG:C00006 + KEGG:C00493
shkk,KEGG:C00002 + KEGG:C00493 <=> KEGG:C00008 + KEGG:C03175
spmdat1,KEGG:C00024 + KEGG:C00315 <=> KEGG:C00010 + KEGG:C00612
spmdat2,KEGG:C00024 + KEGG:C00315 <=> KEGG:C00010 + KEGG:C01029
spms,KEGG:C00134 + KEGG:C01137 <=> KEGG:C00170 + KEGG:C00315
spodm,2 KEGG:C00704 <=> KEGG:C00007 + KEGG:C00027
spodmpp,2 KEGG:C00704 <=> KEGG:C00007 + KEGG:C00027
ssalx,KEGG:C00001 + KEGG:C00003 + KEGG:C00232 <=> KEGG:C00004 + KEGG:C00042
ssaly,KEGG:C00001 + KEGG:C00006 + KEGG:C00232 <=> KEGG:C00005 + KEGG:C00042
sucbzl,KEGG:C00002 + KEGG:C00010 + KEGG:C02730 <=> KEGG:C00013 + KEGG:C00020 + KEGG:C03160
sucbzs,KEGG:C05817 <=> KEGG:C00001 + KEGG:C02730
sucoas,KEGG:C00002 + KEGG:C00010 + KEGG:C00042 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C00091
sucptspp,KEGG:C00074 + KEGG:C00089 <=> KEGG:C00022 + KEGG:C02591
sulri,3 KEGG:C00005 + KEGG:C00094 <=> 3 KEGG:C00001 + 3 KEGG:C00006 + KEGG:C00283
tagurr,KEGG:C00003 + KEGG:C00817 <=> KEGG:C00004 + KEGG:C00558
tala,KEGG:C00118 + KEGG:C05382 <=> KEGG:C00085 + KEGG:C00279
taudo,KEGG:C00007 + KEGG:C00026 + KEGG:C00245 <=> KEGG:C00011 + KEGG:C00042 + KEGG:C00094 + KEGG:C06735
tdp,KEGG:C00001 + KEGG:C00068 <=> KEGG:C00009 + KEGG:C01081
tdpdre,KEGG:C11907 <=> KEGG:C00688
tdpdrr,KEGG:C00005 + KEGG:C00688 <=> KEGG:C00006 + KEGG:C03319
tdpgdh,KEGG:C00842 <=> KEGG:C00001 + KEGG:C11907
tdsk,KEGG:C00002 + KEGG:C04932 <=> KEGG:C00008 + KEGG:C04919
thd2pp,KEGG:C00004 + KEGG:C00006 <=> KEGG:C00003 + KEGG:C00005
thdps,KEGG:C00001 + KEGG:C00091 + KEGG:C03972 <=> KEGG:C00010 + KEGG:C04462
thfat,KEGG:C00001 + KEGG:C00445 <=> KEGG:C03479
thiordxi,KEGG:C00027 + KEGG:C00342 <=> 2 KEGG:C00001 + KEGG:C00343
tmdk1,KEGG:C00002 + KEGG:C00214 <=> KEGG:C00008 + KEGG:C00364
tmdpp,KEGG:C00009 + KEGG:C00214 <=> KEGG:C00178 + KEGG:C00672
tmds,KEGG:C00143 + KEGG:C00365 <=> KEGG:C00364 + KEGG:C00415
tmk,KEGG:C00002 + KEGG:C00378 <=> KEGG:C00008 + KEGG:C01081
tmpk,KEGG:C00002 + KEGG:C01081 <=> KEGG:C00008 + KEGG:C00068
tmppp,KEGG:C04327 + KEGG:C04752 <=> KEGG:C00013 + KEGG:C01081
tpi,KEGG:C00111 <=> KEGG:C00118
trdr,KEGG:C00005 + KEGG:C00343 <=> KEGG:C00006 + KEGG:C00342
tre6pp,KEGG:C00001 + KEGG:C00689 <=> KEGG:C00009 + KEGG:C01083
tre6ps,KEGG:C00029 + KEGG:C00092 <=> KEGG:C00015 + KEGG:C00689
treptspp,KEGG:C00074 + KEGG:C01083 <=> KEGG:C00022 + KEGG:C00689
trps3,KEGG:C03506 <=> KEGG:C00118 + KEGG:C00463
uacgalppp,KEGG:C00001 + KEGG:C00203 <=> KEGG:C00105 + KEGG:C18060
uacgamppp,KEGG:C00001 + KEGG:C00043 <=> KEGG:C00105 + KEGG:C04256
uacmamo,KEGG:C00001 + 2 KEGG:C00003 + KEGG:C01170 <=> 2 KEGG:C00004 + KEGG:C06240
uag2e,KEGG:C00043 <=> KEGG:C01170
uagcvt,KEGG:C00043 + KEGG:C00074 <=> KEGG:C00009 + KEGG:C04631
uagdp,KEGG:C00075 + KEGG:C04256 <=> KEGG:C00013 + KEGG:C00043
uapgr,KEGG:C00005 + KEGG:C04631 <=> KEGG:C00006 + KEGG:C01050
udpg4e,KEGG:C00029 <=> KEGG:C00052
udpgalm,KEGG:C00052 <=> KEGG:C03733
udpgalppp,KEGG:C00001 + KEGG:C00052 <=> KEGG:C00105 + KEGG:C00446
udpgd,KEGG:C00001 + 2 KEGG:C00003 + KEGG:C00029 <=> 2 KEGG:C00004 + KEGG:C00167
udpgppp,KEGG:C00001 + KEGG:C00029 <=> KEGG:C00103 + KEGG:C00105
uglcurppp,KEGG:C00001 + KEGG:C00167 <=> KEGG:C00105 + KEGG:C05385
uglt,KEGG:C00029 + KEGG:C00446 <=> KEGG:C00052 + KEGG:C00103
ugmdds,KEGG:C00002 + KEGG:C00993 + KEGG:C04877 <=> KEGG:C00008 + KEGG:C00009 + KEGG:C04882
uhgada,KEGG:C00001 + KEGG:C04738 <=> KEGG:C00033 + KEGG:C06022
ula4nft,KEGG:C00234 + KEGG:C16153 <=> KEGG:C00101 + KEGG:C16154
umpk,KEGG:C00002 + KEGG:C00105 <=> KEGG:C00008 + KEGG:C00015
upp3mt,2 KEGG:C00019 + KEGG:C01051 <=> 2 KEGG:C00021 + KEGG:C02463
upp3s,KEGG:C01024 <=> KEGG:C00001 + KEGG:C01051
uppdc1,KEGG:C01051 <=> 4 KEGG:C00011 + KEGG:C03263
upprt,KEGG:C00106 + KEGG:C00119 <=> KEGG:C00013 + KEGG:C00105
urdglycd,KEGG:C00003 + KEGG:C00603 <=> KEGG:C00004 + KEGG:C00802
uric,2 KEGG:C00001 + KEGG:C00007 + KEGG:C00366 <=> KEGG:C00011 + KEGG:C00027 + KEGG:C01551
urik2,KEGG:C00044 + KEGG:C00299 <=> KEGG:C00035 + KEGG:C00105
xand,KEGG:C00001 + KEGG:C00003 + KEGG:C00385 <=> KEGG:C00004 + KEGG:C00366
xppt,KEGG:C00119 + KEGG:C00385 <=> KEGG:C00013 + KEGG:C00655
......@@ -30,13 +30,15 @@ del get_versions
import numpy as np
from equilibrator_cache import Compound
from equilibrator_cache import CompoundCache
from equilibrator_cache import CompoundCache, BaseCompound, BaseReaction
from equilibrator_cache.thermodynamic_constants import (
FARADAY, POSSIBLE_REACTION_ARROWS, Q_, R, default_I, default_pH,
default_pMg, default_T, physiological_concentration,
standard_concentration, ureg)
ureg.default_format = ".2f"
ureg.setup_matplotlib(True)
ccache = CompoundCache()
default_phase = 'aqueous'
......@@ -49,6 +51,7 @@ def strip_units(v: np.array) -> np.array:
from equilibrator_api.bounds import Bounds
from equilibrator_api.pathway import Pathway
from equilibrator_api.phased_reaction import PhasedReaction as Reaction
from equilibrator_api.phased_compound import Compound
from equilibrator_api.phased_reaction import Reaction
from equilibrator_api.component_contribution import ComponentContribution
from equilibrator_api.thermo_models import PathwayThermoModel
......@@ -33,7 +33,7 @@ import pkg_resources
from numpy import log
from . import (
Q_, Compound, ccache, default_conc_lb, default_conc_ub,
Q_, BaseCompound, ccache, default_conc_lb, default_conc_ub,
standard_concentration, ureg)
......@@ -44,7 +44,7 @@ class BaseBounds(object):
"""Return a (deep) copy of self."""
raise NotImplementedError
def GetLowerBound(self, compound: Compound):
def GetLowerBound(self, compound: BaseCompound):
"""Get the lower bound for this key.
Args:
......@@ -52,7 +52,7 @@ class BaseBounds(object):
"""
raise NotImplementedError
def GetUpperBound(self, compound: Compound):
def GetUpperBound(self, compound: BaseCompound):
"""Get the upper bound for this key.
Args:
......@@ -62,7 +62,7 @@ class BaseBounds(object):
def GetLowerBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Iterable[float]:
"""Get the bounds for a set of keys in order.
......@@ -73,7 +73,7 @@ class BaseBounds(object):
def GetUpperBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Iterable[float]:
"""Get the bounds for a set of keys in order.
......@@ -82,17 +82,17 @@ class BaseBounds(object):
"""
return map(self.GetUpperBound, compounds)
def GetBoundTuple(self, compound: Compound) -> Tuple[float, float]:
def GetBoundTuple(self, compound: BaseCompound) -> Tuple[float, float]:
"""Get both upper and lower bounds for this key.
:param compound: a Compound object
:param compound: a BaseCompound object
:return: a 2-tuple (lower bound, upper bound)
"""
return self.GetLowerBound(compound), self.GetUpperBound(compound)
def GetBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Tuple[Iterable[float], Iterable[float]]:
"""Get the bounds for a set of compounds.
......@@ -115,7 +115,7 @@ class BaseBounds(object):
def GetLnBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Tuple[Iterable[float], Iterable[float]]:
"""Get the log-bounds for a set of compounds.
......@@ -128,7 +128,7 @@ class BaseBounds(object):
def GetLnLowerBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Iterable[float]:
"""Get the log lower bounds for a set of compounds.
......@@ -140,7 +140,7 @@ class BaseBounds(object):
def GetLnUpperBounds(
self,
compounds: Iterable[Compound]
compounds: Iterable[BaseCompound]
) -> Iterable[float]:
"""Get the log upper bounds for a set of compounds.
......@@ -151,7 +151,7 @@ class BaseBounds(object):
return map(self.conc2ln_conc, ubs)
@ureg.check(None, None, '[concentration]', '[concentration]')
def SetBounds(self, compound: Compound, lb: float, ub: float):
def SetBounds(self, compound: BaseCompound, lb: float, ub: float):
"""Set bounds for a specific key.
Args:
......@@ -173,8 +173,8 @@ class Bounds(BaseBounds):
@ureg.check(None, None, None, '[concentration]', '[concentration]')
def __init__(self,
lower_bounds: Dict[Compound, float] = None,
upper_bounds: Dict[Compound, float] = None,
lower_bounds: Dict[BaseCompound, float] = None,
upper_bounds: Dict[BaseCompound, float] = None,
default_lb: float = default_conc_lb,
default_ub: float = default_conc_ub) -> object:
"""Initialize the bounds object.
......@@ -212,8 +212,8 @@ class Bounds(BaseBounds):
provided
:return: a Bounds object
"""
lbs: Dict[Compound, float] = dict()
ubs: Dict[Compound, float] = dict()
lbs: Dict[BaseCompound, float] = dict()
ubs: Dict[BaseCompound, float] = dict()
bounds_df = pd.read_csv(f)
for row in bounds_df.itertuples():
......@@ -244,7 +244,7 @@ class Bounds(BaseBounds):
bounds_unit: str = None,
default_lb: float = default_conc_lb,
default_ub: float = default_conc_ub
) -> Tuple[object, Dict[Compound, str]]:
) -> Tuple[object, Dict[BaseCompound, str]]:
"""Read bounds from a Pandas DataFrame.
:param df: a pandas.DataFrame with the bounds
......@@ -318,7 +318,7 @@ class Bounds(BaseBounds):
self.default_lb,
self.default_ub)
def GetLowerBound(self, compound: Compound) -> float:
def GetLowerBound(self, compound: BaseCompound) -> float:
"""Get the lower bound for this key.
:param compound: a compound
......@@ -326,7 +326,7 @@ class Bounds(BaseBounds):
"""
return self.lower_bounds.get(compound, self.default_lb)
def GetUpperBound(self, compound: Compound):
def GetUpperBound(self, compound: BaseCompound):
"""Get the upper bound for this key.
:param compound: a compound
......
......@@ -32,7 +32,7 @@ import numpy as np
from component_contribution import GibbsEnergyPredictor
from . import FARADAY, R, default_I, default_pH, default_pMg, default_T, ureg
from .phased_reaction import PhasedReaction
from .phased_reaction import Reaction
class ComponentContribution(object):
......@@ -69,7 +69,7 @@ class ComponentContribution(object):
return R * self.temperature
@staticmethod
def standard_dg(reaction: PhasedReaction) -> Tuple[float, float]:
def standard_dg(reaction: Reaction) -> Tuple[float, float]:
"""Calculate the chemical reaction energies of a reaction.
:param reaction: the input Reaction object
......@@ -87,7 +87,7 @@ class ComponentContribution(object):
@staticmethod
def standard_dg_multi(
reactions: List[PhasedReaction]) -> Tuple[np.array, np.array]:
reactions: List[Reaction]) -> Tuple[np.array, np.array]:
"""Calculate the chemical reaction energies of a list of reactions.
Using the major microspecies of each of the reactants.
......@@ -106,7 +106,7 @@ class ComponentContribution(object):
def standard_dg_prime(
self,
reaction: PhasedReaction
reaction: Reaction
) -> Tuple[float, float]:
"""Calculate the transformed reaction energies of a reaction.
......@@ -134,7 +134,7 @@ class ComponentContribution(object):
def dg_prime(
self,
reaction: PhasedReaction
reaction: Reaction
) -> Tuple[float, float]:
"""Calculate the dG'0 of a single reaction.
......@@ -150,7 +150,7 @@ class ComponentContribution(object):
def standard_dg_prime_multi(
self,
reactions: List[PhasedReaction]
reactions: List[Reaction]
) -> Tuple[np.array, np.array]:
"""Calculate the transformed reaction energies of a list of reactions.
......@@ -179,14 +179,14 @@ class ComponentContribution(object):
def physiological_dg_prime(
self,
reaction: PhasedReaction
reaction: Reaction
) -> Tuple[float, float]:
"""Calculate the dG'm of a single reaction.
Assume all aqueous reactants are at 1 mM, gas reactants at 1 mbar and
the rest at their standard concentration.
:param reaction: an object of type PhasedReaction
:param reaction: an object of type Reaction
:return: a tuple (dG_r_prime, dG_uncertainty) where dG_r_prime is
the estimated Gibbs free energy of reaction and dG_uncertainty is the
standard deviation of estimation. Multiply it by 1.96 to get a 95%
......@@ -198,7 +198,7 @@ class ComponentContribution(object):
self.RT * reaction.physiological_dg_correction()
return physiological_dg_prime, dg_uncertainty
def ln_reversibility_index(self, reaction: PhasedReaction) -> float:
def ln_reversibility_index(self, reaction: Reaction) -> float:
"""Calculate the reversibility index (ln Gamma) of a single reaction.
:return: ln_RI - The reversibility index (in natural log scale).
......@@ -211,10 +211,10 @@ class ComponentContribution(object):
ln_RI = (2.0 / abs_sum_coeff) * physiological_dg_prime / self.RT
return ln_RI
def standard_e_prime(self, reaction: PhasedReaction) -> Tuple[float, float]:
def standard_e_prime(self, reaction: Reaction) -> Tuple[float, float]:
"""Calculate the E'0 of a single half-reaction.
:param reaction: a PhasedReaction object
:param reaction: a Reaction object
:return: a tuple (E0_prime, E0_uncertainty) where E0_prime is
the estimated standard electrostatic potential of reaction and
E0_uncertainty is the standard deviation of estimation. Multiply it
......@@ -237,11 +237,11 @@ class ComponentContribution(object):
def physiological_e_prime(
self,
reaction: PhasedReaction
reaction: Reaction
) -> Tuple[float, float]:
"""Calculate the E'0 of a single half-reaction.
:param reaction: a PhasedReaction object
:param reaction: a Reaction object
:return: a tuple (E0_prime, E0_uncertainty) where E0_prime is
the estimated standard electrostatic potential of reaction and
E0_uncertainty is the standard deviation of estimation. Multiply it
......@@ -264,11 +264,11 @@ class ComponentContribution(object):
def e_prime(
self,
reaction: PhasedReaction
reaction: Reaction
) -> Tuple[float, float]:
"""Calculate the E'0 of a single half-reaction.
:param reaction: a PhasedReaction object
:param reaction: a Reaction object
:return: a tuple (E0_prime, E0_uncertainty) where E0_prime is
the estimated standard electrostatic potential of reaction and
E0_uncertainty is the standard deviation of estimation. Multiply it
......@@ -291,19 +291,19 @@ class ComponentContribution(object):
def dg_analysis(
self,
reaction: PhasedReaction
reaction: Reaction
) -> List[Dict[str, object]]:
"""Get the analysis of the component contribution estimation process.
:param reaction: a PhasedReaction object.
:param reaction: a Reaction object.
:return: the analysis results as a list of dictionaries
"""
return ComponentContribution.predictor.get_dg_analysis(reaction)
def is_using_group_contribution(self, reaction: PhasedReaction) -> bool:
def is_using_group_contribution(self, reaction: Reaction) -> bool:
"""Check whether group contribution is needed to get this reactions' dG.
:param reaction: a PhasedReaction object.
:param reaction: a Reaction object.
:return: true iff group contribution is needed
"""
return ComponentContribution.predictor.is_using_group_contribution(
......
bigg.reaction,formula
ex_14glucan_e,C00718 <=>
ex_fe3hox_e,C06227 <=>
ex_mobd_e,C06232 <=>
14glucanabcpp,C00001 + C00002 + C00718 <=> C00008 + C00009
14glucantexi, <=> C00718
23pde2pp,C00001 + C02355 <=> C01368
23pde4pp,C00001 + C02354 <=> C05822
23pde7pp,C00001 + C02353 <=> C01367
23pde9pp,C00001 + C06194 <=> C06193
2mahmp,C00001 + C04752 <=> C00009 + C04556
3hcinnmh,C00004 + C00007 + C12621 <=> C00001 + C00003 + C12623
3hpppnh,C00004 + C00007 + C11457 <=> C00001 + C00003 + C04044
3kgk,C00002 + C00618 <=> C00008 + C14899
3ntd2pp,C00001 + C01368 <=> C00009 + C00299
3ntd4pp,C00001 + C05822 <=> C00009 + C00475
3ntd7pp,C00001 + C01367 <=> C00009 + C00212
3ntd9pp,C00001 + C06193 <=> C00009 + C00387
3oxcoat,C00010 + C02232 <=> C00024 + C00091
4hthrs,C00001 + C06055 <=> C00009 + C06056
5dglcnr,C00005 + C01062 <=> C00006 + C00257
abutd,C00001 + C00003 + C00555 <=> C00004 + C00334
acacct,C00024 + C00164 <=> C00033 + C00332
acact1r,2 C00024 <=> C00010 + C00332
acact2r,C00024 + C00136 <=> C00010 + C05269
acact3r,C00024 + C05270 <=> C00010 + C05267
acact4r,C00024 + C01944 <=> C00010 + C05265
acact5r,C00024 + C05274 <=> C00010 + C05263
acact6r,C00024 + C01832 <=> C00010 + C05261
acact7r,C00024 + C02593 <=> C00010 + C05259
acact8r,C00010 + C16216 <=> C00024 + C00154
acald,C00003 + C00010 + C00084 <=> C00004 + C00024
acanthat,C00024 + C00108 <=> C00010 + C06332
acbipgt,C00044 + C06509 <=> C00013 + C06510
accoac,C00002 + C00024 + C00288 <=> C00008 + C00009 + C00083
accoal,C00002 + C00010 + C00163 <=> C00008 + C00009 + C00100
acgal1pppp,C00001 + C18060 <=> C00009 + C01132
acgam1pppp,C00001 + C04256 <=> C00009 + C00140
acgamk,C00002 + C00140 <=> C00008 + C00357
acgaptspp,C00074 + C00140 <=> C00022 + C00357
acgk,C00002 + C00624 <=> C00008 + C04133
achbs,C00022 + C00109 <=> C00011 + C00659
ackr,C00002 + C00033 <=> C00008 + C00227
acmanaptspp,C00074 + C00645 <=> C00022 + C04257
acmumptspp,C00074 + C02713 <=> C00022 + C16698
acnml,C00270 <=> C00022 + C00645
acoad1f,C00016 + C00136 <=> C00877 + C01352
acoad2f,C00016 + C05270 <=> C01352 + C05271
acoad3f,C00016 + C01944 <=> C01352 + C05276
acoad4f,C00016 + C05274 <=> C01352 + C05275
acoad5f,C00016 + C01832 <=> C01352 + C03221
acoad6f,C00016 + C02593 <=> C01352 + C05273
acoad7f,C00016 + C00154 <=> C01352 + C05272
acoad8f,C00016 + C00412 <=> C01352 + C16218
acoda,C00001 + C00437 <=> C00033 + C00077
aconis,C02341 <=> C00417
aconmt,C00019 + C02341 <=> C00021 + C11514
aconta,C00158 <=> C00001 + C00417
acontb,C00001 + C00417 <=> C00311
acs,C00002 + C00010 + C00033 <=> C00013 + C00020 + C00024
adcl,C11355 <=> C00022 + C00568
adk1,C00002 + C00020 <=> 2 C00008
adk3,C00020 + C00044 <=> C00008 + C00035
adk4,C00020 + C00081 <=> C00008 + C00104
admdc,C00019 <=> C00011 + C01137
adncyc,C00002 <=> C00013 + C00575
adnk1,C00002 + C00212 <=> C00008 + C00020
adocbik,C00002 + C06508 <=> C00008 + C06509
adocbls,C05775 + C06510 <=> C00144 + C00194
adprdp,C00001 + C00301 <=> C00020 + C03736
adpt,C00119 + C00147 <=> C00013 + C00020
adsk,C00002 + C00224 <=> C00008 + C00053
adsl1r,C03794 <=> C00020 + C00122
adsl2r,C04823 <=> C00122 + C04677
agdc,C00001 + C00357 <=> C00033 + C00352
agmhe,C06397 <=> C06398
agmt,C00001 + C00179 <=> C00086 + C00134
agpr,C00006 + C00009 + C01250 <=> C00005 + C04133
aicart,C00234 + C04677 <=> C00101 + C04734
airc2,C00002 + C00288 + C03373 <=> C00008 + C00009 + C15667
airc3,C04751 <=> C15667
akgdh,C00003 + C00010 + C00026 <=> C00004 + C00011 + C00091
alcd19,C00004 + C00577 <=> C00003 + C00116
alcd2x,C00003 + C00469 <=> C00004 + C00084
aldd19xr,C00001 + C00003 + C00601 <=> C00004 + C00548
aldd2x,C00001 + C00003 + C00084 <=> C00004 + C00033
aldd2y,C00001 + C00006 + C00084 <=> C00005 + C00033
aldd3y,C00001 + C00006 + C00479 <=> C00005 + C00163
aldd4,C00001 + C00003 + C01412 <=> C00004 + C00246
alltn,C00001 + C01551 <=> C00499
alr2,C00005 + C00546 <=> C00006 + C05235
altrh,C00817 <=> C00001 + C00204
amanaper,C04257 <=> C00357
amank,C00002 + C00645 <=> C00008 + C04257
amaotr,C00019 + C01092 <=> C01037 + C04425
ampms2,C00001 + C00003 + C03373 <=> C00004 + 2 C00058 + C04556
ampn,C00001 + C00020 <=> C00147 + C03736
anprt,C00108 + C00119 <=> C00013 + C04302
aobutds,C03214 <=> C00011 + C01888
ap4ah,C00001 + C01260 <=> 2 C00008
ap4as,2 C00002 <=> C00013 + C01260
ap5ah,C00001 + C04058 <=> C00002 + C00008
apcs,C01137 + C01672 <=> C00170 + C16565
appldhr,C00004 + C01888 <=> C00003 + C05771
apraur,C00005 + C01268 <=> C00006 + C04454
arbtptspp,C00074 + C06186 <=> C00022 + C06187
asad,C00006 + C00009 + C00441 <=> C00005 + C03082
ascbpl,C00001 + C16186 <=> C14899
atpm,C00001 + C00002 <=> C00008 + C00009
atpprt,C00002 + C00119 <=> C00013 + C02739
atps4rpp,C00008 + C00009 <=> C00001 + C00002
betaldhx,C00001 + C00003 + C00576 <=> C00004 + C00719
betaldhy,C00001 + C00006 + C00576 <=> C00005 + C00719
bpnt,C00001 + C00054 <=> C00009 + C00020
butct,C00024 + C00246 <=> C00033 + C00136
cat,2 C00027 <=> 2 C00001 + C00007
cdgr,2 C00005 + C15996 <=> 2 C00006 + C16675
cdpmek,C00002 + C11435 <=> C00008 + C11436
chold,C00003 + C00114 <=> C00004 + C00576
chorm,C00251 <=> C00254
chors,C01269 <=> C00009 + C00251
chrpl,C00251 <=> C00022 + C00156
cinndo,C00004 + C00007 + C00423 <=> C00003 + C12622
citl,C00158 <=> C00033 + C00036
cmpn,C00001 + C00055 <=> C00380 + C03736
cpmps,C00001 + C00044 <=> C00013 + C18239
cpppgo,C00007 + C03263 <=> 2 C00001 + 2 C00011 + C01079
cs,C00001 + C00024 + C00036 <=> C00010 + C00158
cyanst,C00177 + C00320 <=> C00094 + C01755
cyanstpp,C00177 + C00320 <=> C00094 + C01755
cytdk2,C00044 + C00475 <=> C00035 + C00055
cytk1,C00002 + C00055 <=> C00008 + C00112
cytk2,C00002 + C00239 <=> C00008 + C00705
dadk,C00002 + C00360 <=> C00008 + C00206
dbts,C00002 + C00011 + C01037 <=> C00008 + C00009 + C01909
ddgalk,C00002 + C01216 <=> C00008 + C01286
ddglk,C00002 + C00204 <=> C00008 + C04442
ddpa,C00001 + C00074 + C00279 <=> C00009 + C04691
ddpgala,C01286 <=> C00022 + C00118
dgk1,C00002 + C00362 <=> C00008 + C00361
dhad1,C04272 <=> C00001 + C00141
dhad2,C04104 <=> C00001 + C00671
dhapt,C00074 + C00184 <=> C00022 + C00111
dhbd,C00003 + C04171 <=> C00004 + C00196
dhbs,C00002 + C00196 <=> C00013 + C04030
dhcind,C00003 + C12622 <=> C00004 + C12623
dhdpry,C00005 + C03340 <=> C00006 + C03972
dhdps,C00022 + C00441 <=> 2 C00001 + C03340
dhfr,C00005 + C00415 <=> C00006 + C00101
dhnpa2r,C04874 <=> C00266 + C01300
dhppd,C00003 + C11588 <=> C00004 + C04044
dhps2,C00568 + C04807 <=> C00013 + C00921
dhptdnr,C00005 + C05649 <=> C00006 + C05650
dhptdnrn,C00004 + C05649 <=> C00003 + C05650
dhqs,C04691 <=> C00009 + C00944
dhqti,C00944 <=> C00001 + C02637
dkglcnr2x,C00004 + C02780 <=> C00003 + C01062
dkglcnr2y,C00005 + C02780 <=> C00006 + C01062
dmatt,C00129 + C00235 <=> C00013 + C00341
dmpps,C00004 + C11811 <=> C00001 + C00003 + C00235
dnmppa,C00001 + C05925 <=> C00009 + C04874
dntppa,C00001 + C04895 <=> C00013 + C05925
dogulnr,C00004 + C04575 <=> C00003 + C00618
dpcoak,C00002 + C00882 <=> C00008 + C00010
drpa,C00673 <=> C00084 + C00118
dtmpk,C00002 + C00364 <=> C00008 + C00363
duradx,C00003 + C00429 <=> C00004 + C00106
durik1,C00002 + C00526 <=> C00008 + C00365
duripp,C00009 + C00526 <=> C00106 + C00672
dutpdp,C00001 + C00460 <=> C00013 + C00365
dxprii,C00005 + C11437 <=> C00006 + C11434
dxps,C00022 + C00118 <=> C00011 + C11437
dxylk,C00002 + C06257 <=> C00008 + C11437
e4pd,C00001 + C00003 + C00279 <=> C00004 + C03393
ecoah1,C01144 <=> C00001 + C00877
ecoah2,C05268 <=> C00001 + C05271
ecoah3,C05266 <=> C00001 + C05276
ecoah4,C05264 <=> C00001 + C05275
ecoah5,C05262 <=> C00001 + C03221
ecoah6,C05260 <=> C00001 + C05273
ecoah7,C05258 <=> C00001 + C05272
ecoah8,C16217 <=> C00001 + C16218
eda,C04442 <=> C00022 + C00118
edd,C00345 <=> C00001 + C04442
eno,C00631 <=> C00001 + C00074
entcs,3 C04030 + 3 C05820 <=> 6 C00020 + C05821
f6pa,C00085 <=> C00118 + C00184
f6pp,C00001 + C00085 <=> C00009 + C00095
facoae100,C00001 + C05274 <=> C00010 + C01571
facoae120,C00001 + C01832 <=> C00010 + C02679
facoae140,C00001 + C02593 <=> C00010 + C06424
facoae160,C00001 + C00154 <=> C00010 + C00249
facoae180,C00001 + C00412 <=> C00010 + C01530
facoae60,C00001 + C05270 <=> C00010 + C01585
facoae80,C00001 + C01944 <=> C00010 + C06423
facoal100t2pp,C00002 + C00010 + C01571 <=> C00013 + C00020 + C05274
facoal120t2pp,C00002 + C00010 + C02679 <=> C00013 + C00020 + C01832
facoal140t2pp,C00002 + C00010 + C06424 <=> C00013 + C00020 + C02593
facoal160t2pp,C00002 + C00010 + C00249 <=> C00013 + C00020 + C00154
facoal180t2pp,C00002 + C00010 + C01530 <=> C00013 + C00020 + C00412
facoal60t2pp,C00002 + C00010 + C01585 <=> C00013 + C00020 + C05270
facoal80t2pp,C00002 + C00010 + C06423 <=> C00013 + C00020 + C01944
fadrx,C00004 + C00016 <=> C00003 + C01352
fadrx2,C00005 + C00016 <=> C00006 + C01352
fba,C00354 <=> C00111 + C00118
fba3,C00447 <=> C00111 + C00279
fbp,C00001 + C00354 <=> C00009 + C00085
fdmo,C00007 + C01847 + C05123 <=> C00001 + C00061 + C00094 + C00266
fdmo2,C00007 + C01847 + C11145 <=> C00001 + C00061 + C00067 + C00094
fdmo6,C00007 + C01847 + C14179 <=> C00001 + C00048 + C00061 + C00094
fe3hoxabcpp,C00001 + C00002 <=> C00008 + C00009 + C06227
fe3hoxtonex,C06227 <=>
fe3ri,C01352 + 2 C14819 <=> C00016 + 2 C14818
feropp,C00007 + 4 C14818 <=> 2 C00001 + 4 C14819
ffsd,C00001 + C02591 <=> C00092 + C00095
fhl,C00058 <=> C00011 + C00282
flvr,C00005 + C00255 <=> C00006 + C01007
flvrx,C00004 + C00255 <=> C00003 + C01007
fmnat,C00002 + C00061 <=> C00013 + C00016
fmnrx,C00004 + C00061 <=> C00003 + C01847
fmnrx2,C00005 + C00061 <=> C00006 + C01847
fometri,C03479 <=> C00001 + C00445
forct,C00209 + C00798 <=> C00058 + C00313
frupts2pp,C00074 + C00095 <=> C00022 + C00085
fthfd,C00001 + C00234 <=> C00058 + C00101
fthfli,C00002 + C00058 + C00101 <=> C00008 + C00009 + C00234
g1pact,C00024 + C03783 <=> C00010 + C04256
g1ptt,C00103 + C00459 <=> C00013 + C00842
g1sat,C03741 <=> C00430
g2pp,C00001 + C02979 <=> C00009 + C00116
g2pppp,C00001 + C02979 <=> C00009 + C00116
g3pd2,C00006 + C00093 <=> C00005 + C00111
g3pt,C00001 + C00093 <=> C00009 + C00116
g5sads,C01165 <=> C00001 + C03912
g5sd,C00005 + C03287 <=> C00006 + C00009 + C01165
g6pdh2r,C00006 + C00092 <=> C00005 + C01236
gal1pppp,C00001 + C00446 <=> C00009 + C00124
galkr,C00002 + C00124 <=> C00008 + C00446
galm2pp,C00962 <=> C00124
galtptspp,C00074 + C01697 <=> C00022 + C06311
galui,C00075 + C00103 <=> C00013 + C00029
gamptspp,C00074 + C00329 <=> C00022 + C00352
gapd,C00003 + C00009 + C00118 <=> C00004 + C00236
garft,C00234 + C03838 <=> C00101 + C04376
gart,C00002 + C00058 + C03838 <=> C00008 + C00009 + C04376
gcaldd,C00001 + C00003 + C00266 <=> C00004 + C00160
gdmane,C01222 <=> C14830
gdpdpk,C00002 + C00035 <=> C00020 + C01228
gdpmnh,C00001 + C00096 <=> C00035 + C00159
gdpmnp,C00001 + C00096 <=> C00144 + C00636
gdptpdp,C00001 + C04494 <=> C00013 + C00044
gggabadr,C00001 + C00006 + C15700 <=> C00005 + C15767
ghbdhx,C00004 + C00232 <=> C00003 + C00989
gk1,C00002 + C00144 <=> C00008 + C00035
glcral,C00679 <=> C00022 + C01146
glcrd,C00818 <=> C00001 + C00679
glgc,C00002 + C00103 <=> C00013 + C00498
glxcl,2 C00048 <=> C00011 + C01146
glyat,C00024 + C00037 <=> C00010 + C03214
glycdx,C00003 + C00116 <=> C00004 + C00184
glycltdx,C00004 + C00048 <=> C00003 + C00160
glycltdy,C00005 + C00048 <=> C00006 + C00160
glyk,C00002 + C00116 <=> C00008 + C00093
gmand,C00096 <=> C00001 + C01222
gmhepat,C00002 + C07838 <=> C00013 + C06397
gmhepk,C00002 + C07836 <=> C00008 + C11472
gmheppa,C00001 + C11472 <=> C00009 + C07838
gnk,C00002 + C00257 <=> C00008 + C00345
gofucr,C00005 + C14830 <=> C00006 + C00325
gp4gh,C00001 + C01261 <=> 2 C00035
gpdda1,C00001 + C00670 <=> C00093 + C00114
gpdda1pp,C00001 + C00670 <=> C00093 + C00114
gpdda2,C00001 + C01233 <=> C00093 + C00189
gpdda2pp,C00001 + C01233 <=> C00093 + C00189
gpdda4,C00001 + C03274 <=> C00093 + C00116
gpdda4pp,C00001 + C03274 <=> C00093 + C00116
gpdda5,C00001 + C01225 <=> C00093 + C00137
gpdda5pp,C00001 + C01225 <=> C00093 + C00137
grtt,C00129 + C00341 <=> C00013 + C00448
gsnk,C00002 + C00387 <=> C00008 + C00144
gtpci,C00001 + C00044 <=> C00058 + C04895
gtpcii2,3 C00001 + C00044 <=> C00013 + C00058 + C01304
gtpdpdp,C00001 + C04494 <=> C00009 + C01228
gtpdpk,C00002 + C00044 <=> C00020 + C04494
guacyc,C00044 <=> C00013 + C00942
guaprt,C00119 + C00242 <=> C00013 + C00144
gui1,C00191 <=> C00905
gur1pppp,C00001 + C05385 <=> C00009 + C00191
h2so,2 C00007 + C00283 <=> C00059
hacd1,C00004 + C00332 <=> C00003 + C01144
hacd2,C00004 + C05269 <=> C00003 + C05268
hacd3,C00004 + C05267 <=> C00003 + C05266
hacd4,C00004 + C05265 <=> C00003 + C05264
hacd5,C00004 + C05263 <=> C00003 + C05262
hacd6,C00004 + C05261 <=> C00003 + C05260
hacd7,C00004 + C05259 <=> C00003 + C05258
hacd8,C00004 + C16216 <=> C00003 + C16217
hadpcoadh3,C00003 + C14145 <=> C00004 + C02232
hbzopt,C00156 + C04146 <=> C00013 + C05809
hco3e,C00001 + C00011 <=> C00288
hetzk,C00002 + C04294 <=> C00008 + C04327
hex4,C00002 + C00159 <=> C00008 + C00275
hex7,C00002 + C00095 <=> C00008 + C00085
histp,C00001 + C01100 <=> C00009 + C00860
hknddh,C00001 + C04479 <=> C00042 + C00596
hmpk1,C00002 + C01279 <=> C00008 + C04556
hopntal,C03589 <=> C00022 + C00084
hppk2,C00002 + C01300 <=> C00020 + C04807
hpppndo,C00007 + C04044 <=> C04479
hpyri,C00168 <=> C01146
hxand,C00001 + C00003 + C00262 <=> C00004 + C00385
hxct,C00024 + C01585 <=> C00033 + C05270
hxprt,C00119 + C00262 <=> C00013 + C00130
hypoe,C00001 + C00647 <=> C00009 + C00534
icdhyr,C00006 + C00311 <=> C00005 + C00011 + C00026
ichors,C00251 <=> C00885
ichorsi,C00251 <=> C00885
ichort,C00001 + C00885 <=> C00022 + C04171
icl,C00311 <=> C00042 + C00048
igpdh,C04666 <=> C00001 + C01267
igps,C01302 <=> C00001 + C00011 + C03506
impc,C00001 + C00130 <=> C04734
impd,C00001 + C00003 + C00130 <=> C00004 + C00655
insk,C00002 + C00294 <=> C00008 + C00130
ipddi,C00129 <=> C00235
ipdps,C00004 + C11811 <=> C00001 + C00003 + C00129
ipmd,C00003 + C04411 <=> C00004 + C04236
ippmia,C04411 <=> C00001 + C02631
ippmib,C00001 + C02631 <=> C02504
ipps,C00001 + C00024 + C00141 <=> C00010 + C02504
kara2,C00005 + C00659 <=> C00006 + C04104
kdoct2,C00063 + C01187 <=> C00013 + C04121